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<meta name="author" content="Magdalena Breyer, Tabea Palmtag, Delia Zollinger" />

<meta name="date" content="2025-09-16" />

<title>Narratives of Backlash? Perceptions of Changing Status Hierarchies in Open-Ended Survey Responses</title>

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) 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Cv3aCgUIAQgIFID+lAgKCggBbAgIpAgIpAgAAAAD//D/8AS6BLoACQANABAAAAAyHwEWFA8BJzcTAScJAQUTA+AmDpkNDWPWXyL9mdYCZv4f/rNuBLoNmQ4mDlzWYP50/ZrWAmb8anABTwAAAAEAAAAABLAEsAAPAAABETMyFh0BITU0NjsBEQEhArz6FR384B0V+v4MBLACiv3aHRUyMhUdAiYCJgAAAAEADgAIBEwEnAAfAAABJTYWFREUBgcGLgE2NzYXEQURFAYHBi4BNjc2FxE0NgFwAoUnMFNGT4gkV09IQv2oWEFPiCRXT0hCHQP5ow8eIvzBN1EXGSltchkYEAIJm/2iKmAVGilucRoYEQJ/JioAAAACAAn/+AS7BKcAHQApAAAAMh4CFQcXFAcBFgYPAQYiJwEGIycHIi4CND4BBCIOARQeATI+ATQmAZDItoNOAQFOARMXARY7GikT/u13jgUCZLaDTk6DAXKwlFZWlLCUVlYEp06DtmQCBY15/u4aJRg6FBQBEk0BAU6Dtsi2g1tWlLCUVlaUsJQAAQBkAFgErwREABkAAAE+Ah4CFRQOAwcuBDU0PgIeAQKJMHt4dVg2Q3mEqD4+p4V4Qzhadnh5A7VESAUtU3ZAOXmAf7JVVbJ/gHk5QHZTLQVIAAAAAf/TAF4EewSUABgAAAETNjIXEyEyFgcFExYGJyUFBiY3EyUmNjMBl4MHFQeBAaUVBhH+qoIHDxH+qf6qEQ8Hgv6lEQYUAyABYRMT/p8RDPn+bxQLDPb3DAsUAZD7DBEAAv/TAF4EewSUABgAIgAAARM2MhcTITIWBwUTFgYnJQUGJjcTJSY2MwUjFwc3Fyc3IycBl4MHFQeBAaUVBhH+qoIHDxH+qf6qEQ8Hgv6lEQYUAfPwxUrBw0rA6k4DIAFhExP+nxEM+f5vFAsM9vcMCxQBkPsMEWSO4ouM5YzTAAABAAAAAASwBLAAJgAAATIWHQEUBiMVFBYXBR4BHQEUBiMhIiY9ATQ2NyU+AT0BIiY9ATQ2Alh8sD4mDAkBZgkMDwr7ggoPDAkBZgkMJj6wBLCwfPouaEsKFwbmBRcKXQoPDwpdChcF5gYXCktoLvp8sAAAAA0AAAAABLAETAAPABMAIwAnACsALwAzADcARwBLAE8AUwBXAAATITIWFREUBiMhIiY1ETQ2FxUzNSkBIgYVERQWMyEyNjURNCYzFTM1BRUzNSEVMzUFFTM1IRUzNQchIgYVERQWMyEyNjURNCYFFTM1IRUzNQUVMzUhFTM1GQR+Cg8PCvuCCg8PVWQCo/3aCg8PCgImCg8Pc2T8GGQDIGT8GGQDIGTh/doKDw8KAiYKDw/872QDIGT8GGQDIGQETA8K++YKDw8KBBoKD2RkZA8K/qIKDw8KAV4KD2RkyGRkZGTIZGRkZGQPCv6iCg8PCgFeCg9kZGRkZMhkZGRkAAAEAAAAAARMBEwADwAfAC8APwAAEyEyFhURFAYjISImNRE0NikBMhYVERQGIyEiJjURNDYBITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NjIBkBUdHRX+cBUdHQJtAZAVHR0V/nAVHR39vQGQFR0dFf5wFR0dAm0BkBUdHRX+cBUdHQRMHRX+cBUdHRUBkBUdHRX+cBUdHRUBkBUd/agdFf5wFR0dFQGQFR0dFf5wFR0dFQGQFR0AAAkAAAAABEwETAAPAB8ALwA/AE8AXwBvAH8AjwAAEzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2MsgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR0ETB0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHf5wHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUd/nAdFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0ABgAAAAAEsARMAA8AHwAvAD8ATwBfAAATMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYyyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHQRMHRXIFR0dFcgVHR0VyBUdHRXIFR3+cB0VyBUdHRXIFR0dFcgVHR0VyBUd/nAdFcgVHR0VyBUdHRXIFR0dFcgVHQAAAAABACYALAToBCAAFwAACQE2Mh8BFhQHAQYiJwEmND8BNjIfARYyAdECOwgUB7EICPzxBxUH/oAICLEHFAirBxYB3QI7CAixBxQI/PAICAGACBQHsQgIqwcAAQBuAG4EQgRCACMAAAEXFhQHCQEWFA8BBiInCQEGIi8BJjQ3CQEmND8BNjIXCQE2MgOIsggI/vUBCwgIsggVB/70/vQHFQiyCAgBC/71CAiyCBUHAQwBDAcVBDuzCBUH/vT+9AcVCLIICAEL/vUICLIIFQcBDAEMBxUIsggI/vUBDAcAAwAX/+sExQSZABkAJQBJAAAAMh4CFRQHARYUDwEGIicBBiMiLgI0PgEEIg4BFB4BMj4BNCYFMzIWHQEzMhYdARQGKwEVFAYrASImPQEjIiY9ATQ2OwE1NDYBmcSzgk1OASwICG0HFQj+1HeOYrSBTU2BAW+zmFhYmLOZWFj+vJYKD0sKDw8KSw8KlgoPSwoPDwpLDwSZTYKzYo15/tUIFQhsCAgBK01NgbTEs4JNWJmzmFhYmLOZIw8KSw8KlgoPSwoPDwpLDwqWCg9LCg8AAAMAF//rBMUEmQAZACUANQAAADIeAhUUBwEWFA8BBiInAQYjIi4CND4BBCIOARQeATI+ATQmBSEyFh0BFAYjISImPQE0NgGZxLOCTU4BLAgIbQcVCP7Ud45itIFNTYEBb7OYWFiYs5lYWP5YAV4KDw8K/qIKDw8EmU2Cs2KNef7VCBUIbAgIAStNTYG0xLOCTViZs5hYWJizmYcPCpYKDw8KlgoPAAAAAAIAFwAXBJkEsAAPAC0AAAEzMhYVERQGKwEiJjURNDYFNRYSFRQOAiIuAjU0EjcVDgEVFB4BMj4BNTQmAiZkFR0dFWQVHR0BD6fSW5vW6tabW9KnZ3xyxejFcnwEsB0V/nAVHR0VAZAVHeGmPv7ZuHXWm1tbm9Z1uAEnPqY3yHh0xXJyxXR4yAAEAGQAAASwBLAADwAfAC8APwAAATMyFhURFAYrASImNRE0NgEzMhYVERQGKwEiJjURNDYBMzIWFREUBisBIiY1ETQ2BTMyFh0BFAYrASImPQE0NgQBlgoPDwqWCg8P/t6WCg8PCpYKDw/+3pYKDw8KlgoPD/7elgoPDwqWCg8PBLAPCvuCCg8PCgR+Cg/+cA8K/RIKDw8KAu4KD/7UDwr+PgoPDwoBwgoPyA8K+goPDwr6Cg8AAAAAAgAaABsElgSWAEcATwAAATIfAhYfATcWFwcXFh8CFhUUDwIGDwEXBgcnBwYPAgYjIi8CJi8BByYnNycmLwImNTQ/AjY/ASc2Nxc3Nj8CNhIiBhQWMjY0AlghKSYFMS0Fhj0rUAMZDgGYBQWYAQ8YA1AwOIYFLDIFJisfISkmBTEtBYY8LFADGQ0ClwYGlwINGQNQLzqFBS0xBSYreLJ+frJ+BJYFmAEOGQJQMDmGBSwxBiYrHiIoJgYxLAWGPSxRAxkOApcFBZcCDhkDUTA5hgUtMAYmKiAhKCYGMC0Fhj0sUAIZDgGYBf6ZfrF+frEABwBkAAAEsAUUABMAFwAhACUAKQAtADEAAAEhMhYdASEyFh0BITU0NjMhNTQ2FxUhNQERFAYjISImNREXETMRMxEzETMRMxEzETMRAfQBLCk7ARMKD/u0DwoBEzspASwBLDsp/UQpO2RkZGRkZGRkBRQ7KWQPCktLCg9kKTtkZGT+1PzgKTs7KQMgZP1EArz9RAK8/UQCvP1EArwAAQAMAAAFCATRAB8AABMBNjIXARYGKwERFAYrASImNREhERQGKwEiJjURIyImEgJsCBUHAmAIBQqvDwr6Cg/+1A8K+goPrwoFAmoCYAcH/aAICv3BCg8PCgF3/okKDw8KAj8KAAIAZAAAA+gEsAARABcAAAERFBYzIREUBiMhIiY1ETQ2MwEjIiY9AQJYOykBLB0V/OAVHR0VA1L6FR0EsP5wKTv9dhUdHRUETBUd/nAdFfoAAwAXABcEmQSZAA8AGwAwAAAAMh4CFA4CIi4CND4BBCIOARQeATI+ATQmBTMyFhURMzIWHQEUBisBIiY1ETQ2AePq1ptbW5vW6tabW1ubAb/oxXJyxejFcnL+fDIKD68KDw8K+goPDwSZW5vW6tabW1ub1urWmztyxejFcnLF6MUNDwr+7Q8KMgoPDwoBXgoPAAAAAAL/nAAABRQEsAALAA8AACkBAyMDIQEzAzMDMwEDMwMFFP3mKfIp/eYBr9EVohTQ/p4b4BsBkP5wBLD+1AEs/nD+1AEsAAAAAAIAZAAABLAEsAAVAC8AAAEzMhYVETMyFgcBBiInASY2OwERNDYBMzIWFREUBiMhIiY1ETQ2OwEyFh0BITU0NgImyBUdvxQLDf65DSYN/rkNCxS/HQJUMgoPDwr75goPDwoyCg8DhA8EsB0V/j4XEP5wEBABkBAXAcIVHfzgDwr+ogoPDwoBXgoPDwqvrwoPAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgUzMhYVETMyFgcDBiInAyY2OwERNDYB4+rWm1tbm9bq1ptbW5sBv+jFcnLF6MVycv58lgoPiRUKDd8NJg3fDQoViQ8EmVub1urWm1tbm9bq1ps7csXoxXJyxejFDQ8K/u0XEP7tEBABExAXARMKDwAAAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JiUTFgYrAREUBisBIiY1ESMiJjcTNjIB4+rWm1tbm9bq1ptbW5sBv+jFcnLF6MVycv7n3w0KFYkPCpYKD4kVCg3fDSYEmVub1urWm1tbm9bq1ps7csXoxXJyxejFAf7tEBf+7QoPDwoBExcQARMQAAAAAAIAAAAABLAEsAAZADkAABMhMhYXExYVERQGBwYjISImJyY1EzQ3Ez4BBSEiBgcDBhY7ATIWHwEeATsBMjY/AT4BOwEyNicDLgHhAu4KEwO6BwgFDBn7tAweAgYBB7kDEwKX/dQKEgJXAgwKlgoTAiYCEwr6ChMCJgITCpYKDAJXAhIEsA4K/XQYGf5XDB4CBggEDRkBqRkYAowKDsgOC/4+Cw4OCpgKDg4KmAoODgsBwgsOAAMAFwAXBJkEmQAPABsAJwAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgUXFhQPAQYmNRE0NgHj6tabW1ub1urWm1tbmwG/6MVycsXoxXJy/ov9ERH9EBgYBJlbm9bq1ptbW5vW6tabO3LF6MVycsXoxV2+DCQMvgwLFQGQFQsAAQAXABcEmQSwACgAAAE3NhYVERQGIyEiJj8BJiMiDgEUHgEyPgE1MxQOAiIuAjQ+AjMyA7OHBwsPCv6WCwQHhW2BdMVycsXoxXKWW5vW6tabW1ub1nXABCSHBwQL/pYKDwsHhUxyxejFcnLFdHXWm1tbm9bq1ptbAAAAAAIAFwABBJkEsAAaADUAAAE3NhYVERQGIyEiJj8BJiMiDgEVIzQ+AjMyEzMUDgIjIicHBiY1ETQ2MyEyFg8BFjMyPgEDs4cHCw8L/pcLBAeGboF0xXKWW5vWdcDrllub1nXAnIYHCw8LAWgKBQiFboJ0xXIEJIcHBAv+lwsPCweGS3LFdHXWm1v9v3XWm1t2hggFCgFoCw8LB4VMcsUAAAAKAGQAAASwBLAADwAfAC8APwBPAF8AbwB/AI8AnwAAEyEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0NgczMhYdARQGKwEiJj0BNDYzITIWHQEUBiMhIiY9ATQ2BzMyFh0BFAYrASImPQE0NjMhMhYdARQGIyEiJj0BNDYHMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0Nn0EGgoPDwr75goPDwPA/K4KDw8KA1IKDw/9CDIKDw8KMgoPD9IBwgoPDwr+PgoPD74yCg8PCjIKDw/SAcIKDw8K/j4KDw++MgoPDwoyCg8P0gHCCg8PCv4+Cg8PvjIKDw8KMgoPD9IBwgoPDwr+PgoPDwSwDwr7ggoPDwoEfgoPyA8K/K4KDw8KA1IKD2QPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKDwAAAAACAAAAAARMBLAAGQAjAAABNTQmIyEiBh0BIyIGFREUFjMhMjY1ETQmIyE1NDY7ATIWHQEDhHVT/tRSdmQpOzspA4QpOzsp/ageFMgUHgMgyFN1dlLIOyn9qCk7OykCWCk7lhUdHRWWAAIAZAAABEwETAAJADcAABMzMhYVESMRNDYFMhcWFREUBw4DIyIuAScuAiMiBwYjIicmNRE+ATc2HgMXHgIzMjc2fTIKD2QPA8AEBRADIUNAMRwaPyonKSxHHlVLBwgGBQ4WeDsXKC4TOQQpLUUdZ1AHBEwPCvvNBDMKDzACBhH+WwYGO1AkDQ0ODg8PDzkFAwcPAbY3VwMCAwsGFAEODg5XCAAAAwAAAAAEsASXACEAMQBBAAAAMh4CFREUBisBIiY1ETQuASAOARURFAYrASImNRE0PgEDMzIWFREUBisBIiY1ETQ2ITMyFhURFAYrASImNRE0NgHk6N6jYw8KMgoPjeT++uSNDwoyCg9joyqgCAwMCKAIDAwCYKAIDAwIoAgMDASXY6PedP7UCg8PCgEsf9FyctF//tQKDw8KASx03qP9wAwI/jQIDAwIAcwIDAwI/jQIDAwIAcwIDAAAAAACAAAA0wRHA90AFQA5AAABJTYWFREUBiclJisBIiY1ETQ2OwEyBTc2Mh8BFhQPARcWFA8BBiIvAQcGIi8BJjQ/AScmND8BNjIXAUEBAgkMDAn+/hUZ+goPDwr6GQJYeAcUByIHB3h4BwciBxQHeHgHFAciBwd3dwcHIgcUBwMurAYHCv0SCgcGrA4PCgFeCg+EeAcHIgcUB3h4BxQHIgcHd3cHByIHFAd4eAcUByIICAAAAAACAAAA0wNyA90AFQAvAAABJTYWFREUBiclJisBIiY1ETQ2OwEyJTMWFxYVFAcGDwEiLwEuATc2NTQnJjY/ATYBQQECCQwMCf7+FRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcDLqwGBwr9EgoHBqwODwoBXgoPZAEJgaGafwkBAQYXBxMIZ36EaggUBxYFAAAAAAMAAADEBGID7AAbADEASwAAATMWFxYVFAYHBgcjIi8BLgE3NjU0JicmNj8BNgUlNhYVERQGJyUmKwEiJjURNDY7ATIlMxYXFhUUBwYPASIvAS4BNzY1NCcmNj8BNgPHAwsGh0RABwoDCQcqCAIGbzs3BgIJKgf9ggECCQwMCf7+FRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcD7AEJs9lpy1QJAQYiBhQIlrJarEcJFAYhBb6sBgcK/RIKBwasDg8KAV4KD2QBCYGhmn8JAQEGFwcTCGd+hGoIFQYWBQAAAAANAAAAAASwBLAACQAVABkAHQAhACUALQA7AD8AQwBHAEsATwAAATMVIxUhFSMRIQEjFTMVIREjESM1IQURIREhESERBSM1MwUjNTMBMxEhETM1MwEzFSMVIzUjNTM1IzUhBREhEQcjNTMFIzUzASM1MwUhNSEB9GRk/nBkAfQCvMjI/tTIZAJY+7QBLAGQASz84GRkArxkZP1EyP4MyGQB9MhkyGRkyAEs/UQBLGRkZAOEZGT+DGRkAfT+1AEsA4RkZGQCWP4MZMgBLAEsyGT+1AEs/tQBLMhkZGT+DP4MAfRk/tRkZGRkyGTI/tQBLMhkZGT+1GRkZAAAAAAJAAAAAASwBLAAAwAHAAsADwATABcAGwAfACMAADcjETMTIxEzASMRMxMjETMBIxEzASE1IRcjNTMXIzUzBSM1M2RkZMhkZAGQyMjIZGQBLMjI/OD+1AEsyGRkyGRkASzIyMgD6PwYA+j8GAPo/BgD6PwYA+j7UGRkW1tbW1sAAAIAAAAKBKYEsAANABUAAAkBFhQHAQYiJwETNDYzBCYiBhQWMjYB9AKqCAj+MAgUCP1WAQ8KAUM7Uzs7UzsEsP1WCBQI/jAICAKqAdsKD807O1Q7OwAAAAADAAAACgXSBLAADQAZACEAAAkBFhQHAQYiJwETNDYzIQEWFAcBBiIvAQkBBCYiBhQWMjYB9AKqCAj+MAgUCP1WAQ8KAwYCqggI/jAIFAg4Aaj9RP7TO1M7O1M7BLD9VggUCP4wCAgCqgHbCg/9VggUCP4wCAg4AaoCvM07O1Q7OwAAAAABAGQAAASwBLAAJgAAASEyFREUDwEGJjURNCYjISIPAQYWMyEyFhURFAYjISImNRE0PwE2ASwDOUsSQAgKDwr9RBkSQAgFCgK8Cg8PCvyuCg8SixIEsEv8fBkSQAgFCgO2Cg8SQAgKDwr8SgoPDwoDzxkSixIAAAABAMj//wRMBLAACgAAEyEyFhURCQERNDb6AyAVHf4+/j4dBLAdFfuCAbz+QwR/FR0AAAAAAwAAAAAEsASwABUARQBVAAABISIGBwMGHwEeATMhMjY/ATYnAy4BASMiBg8BDgEjISImLwEuASsBIgYVERQWOwEyNj0BNDYzITIWHQEUFjsBMjY1ETQmASEiBg8BBhYzITI2LwEuAQM2/kQLEAFOBw45BhcKAcIKFwY+DgdTARABVpYKFgROBBYK/doKFgROBBYKlgoPDwqWCg8PCgLuCg8PCpYKDw/+sf4MChMCJgILCgJYCgsCJgITBLAPCv7TGBVsCQwMCWwVGAEtCg/+cA0JnAkNDQmcCQ0PCv12Cg8PCpYKDw8KlgoPDwoCigoP/agOCpgKDg4KmAoOAAAAAAQAAABkBLAETAAdACEAKQAxAAABMzIeAh8BMzIWFREUBiMhIiY1ETQ2OwE+BAEVMzUEIgYUFjI2NCQyFhQGIiY0AfTIOF00JAcGlik7Oyn8GCk7OymWAgknM10ByGT+z76Hh76H/u9WPDxWPARMKTs7FRQ7Kf2oKTs7KQJYKTsIG0U1K/7UZGRGh76Hh74IPFY8PFYAAAAAAgA1AAAEsASvACAAIwAACQEWFx4BHwEVITUyNi8BIQYHBh4CMxUhNTY3PgE/AQEDIQMCqQGBFCgSJQkK/l81LBFS/nk6IgsJKjIe/pM4HAwaBwcBj6wBVKIEr/waMioTFQECQkJXLd6RWSIuHAxCQhgcDCUNDQPu/VoByQAAAAADAGQAAAPwBLAAJwAyADsAAAEeBhUUDgMjITU+ATURNC4EJzUFMh4CFRQOAgclMzI2NTQuAisBETMyNjU0JisBAvEFEzUwOyodN1htbDD+DCk7AQYLFyEaAdc5dWM+Hy0tEP6Pi05pESpTPnbYUFJ9Xp8CgQEHGB0zOlIuQ3VONxpZBzMoAzsYFBwLEAkHRwEpSXNDM1s6KwkxYUopOzQb/K5lUFqBAAABAMgAAANvBLAAGQAAARcOAQcDBhYXFSE1NjcTNjQuBCcmJzUDbQJTQgeECSxK/gy6Dq0DAw8MHxUXDQYEsDkTNSj8uTEoBmFhEFIDQBEaExAJCwYHAwI5AAAAAAL/tQAABRQEsAAlAC8AAAEjNC4FKwERFBYfARUhNTI+AzURIyIOBRUjESEFIxEzByczESM3BRQyCAsZEyYYGcgyGRn+cAQOIhoWyBkYJhMZCwgyA+j7m0tLfX1LS30DhBUgFQ4IAwH8rhYZAQJkZAEFCRUOA1IBAwgOFSAVASzI/OCnpwMgpwACACH/tQSPBLAAJQAvAAABIzQuBSsBERQWHwEVITUyPgM1ESMiDgUVIxEhEwc1IRUnNxUhNQRMMggLGRMmGBnIMhkZ/nAEDiIaFsgZGCYTGQsIMgPoQ6f84KenAyADhBUgFQ4IAwH9dhYZAQJkZAEFCRUOAooBAwgOFSAVASz7gn1LS319S0sABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyAlgVHR0V/agVHR0VA+gVHR0V/BgVHR0VAyAVHR0V/OAVHR0VBEwVHR0V+7QVHR0ETB0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYDITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NgMhMhYdARQGIyEiJj0BNDb6ArwVHR0V/UQVHR2zBEwVHR0V+7QVHR3dArwVHR0V/UQVHR2zBEwVHR0V+7QVHR0ETB0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AAAE1NDYzITIWHQEUBiMhIiYBNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJgE1NDYzITIWHQEUBiMhIiYB9B0VAlgVHR0V/agVHf5wHRUD6BUdHRX8GBUdyB0VAyAVHR0V/OAVHf7UHRUETBUdHRX7tBUdA7ZkFR0dFWQVHR3+6WQVHR0VZBUdHf7pZBUdHRVkFR0d/ulkFR0dFWQVHR0AAAQAAAAABLAETAAPAB8ALwA/AAATITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2MgRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dBEwdFWQVHR0VZBUd/tQdFWQVHR0VZBUd/tQdFWQVHR0VZBUd/tQdFWQVHR0VZBUdAAgAAAAABLAETAAPAB8ALwA/AE8AXwBvAH8AABMzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2MmQVHR0VZBUdHQFBAyAVHR0V/OAVHR3+6WQVHR0VZBUdHQFBAyAVHR0V/OAVHR3+6WQVHR0VZBUdHQFBAyAVHR0V/OAVHR3+6WQVHR0VZBUdHQFBAyAVHR0V/OAVHR0ETB0VZBUdHRVkFR0dFWQVHR0VZBUd/tQdFWQVHR0VZBUdHRVkFR0dFWQVHf7UHRVkFR0dFWQVHR0VZBUdHRVkFR3+1B0VZBUdHRVkFR0dFWQVHR0VZBUdAAAG/5wAAASwBEwAAwATACMAKgA6AEoAACEjETsCMhYdARQGKwEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2BQc1IzUzNQUhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2AZBkZJZkFR0dFWQVHR0VAfQVHR0V/gwVHR3++qfIyAHCASwVHR0V/tQVHR0VAlgVHR0V/agVHR0ETB0VZBUdHRVkFR3+1B0VZBUdHRVkFR36fUtkS68dFWQVHR0VZBUd/tQdFWQVHR0VZBUdAAAABgAAAAAFFARMAA8AEwAjACoAOgBKAAATMzIWHQEUBisBIiY9ATQ2ASMRMwEhMhYdARQGIyEiJj0BNDYFMxUjFSc3BSEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyZBUdHRVkFR0dA2dkZPyuAfQVHR0V/gwVHR0EL8jIp6f75gEsFR0dFf7UFR0dFQJYFR0dFf2oFR0dBEwdFWQVHR0VZBUd+7QETP7UHRVkFR0dFWQVHchkS319rx0VZBUdHRVkFR3+1B0VZBUdHRVkFR0AAAAAAgAAAMgEsAPoAA8AEgAAEyEyFhURFAYjISImNRE0NgkCSwLuHywsH/0SHywsBIT+1AEsA+gsH/12HywsHwKKHyz9RAEsASwAAwAAAAAEsARMAA8AFwAfAAATITIWFREUBiMhIiY1ETQ2FxE3BScBExEEMhYUBiImNCwEWBIaGhL7qBIaGkr3ASpKASXs/NJwTk5wTgRMGhL8DBIaGhID9BIaZP0ftoOcAT7+4AH0dE5vT09vAAAAAAIA2wAFBDYEkQAWAB4AAAEyHgEVFAcOAQ8BLgQnJjU0PgIWIgYUFjI2NAKIdcZzRkWyNjYJIV5YbSk8RHOft7eCgreCBJF4ynVzj23pPz4IIWZomEiEdVijeUjDgriBgbgAAAACABcAFwSZBJkADwAXAAAAMh4CFA4CIi4CND4BAREiDgEUHgEB4+rWm1tbm9bq1ptbW5sBS3TFcnLFBJlbm9bq1ptbW5vW6tab/G8DVnLF6MVyAAACAHUAAwPfBQ8AGgA1AAABHgYVFA4DBy4DNTQ+BQMOAhceBBcWNj8BNiYnLgInJjc2IyYCKhVJT1dOPiUzVnB9P1SbfEokP0xXUEm8FykoAwEbITEcExUWAgYCCQkFEikMGiACCAgFD0iPdXdzdYdFR4BeRiYEBTpjl1lFh3ZzeHaQ/f4hS4I6JUEnIw4IBwwQIgoYBwQQQSlZtgsBAAAAAwAAAAAEywRsAAwAKgAvAAABNz4CHgEXHgEPAiUhMhcHISIGFREUFjMhMjY9ATcRFAYjISImNRE0NgkBBzcBA+hsAgYUFR0OFgoFBmz9BQGQMje7/pApOzspAfQpO8i7o/5wpbm5Azj+lqE3AWMD9XMBAgIEDw4WKgsKc8gNuzsp/gwpOzsptsj+tKW5uaUBkKW5/tf+ljKqAWMAAgAAAAAEkwRMABsANgAAASEGByMiBhURFBYzITI2NTcVFAYjISImNRE0NgUBFhQHAQYmJzUmDgMHPgY3NT4BAV4BaaQ0wyk7OykB9Ck7yLml/nClubkCfwFTCAj+rAcLARo5ZFRYGgouOUlARioTAQsETJI2Oyn+DCk7OymZZ6W5uaUBkKW5G/7TBxUH/s4GBAnLAQINFjAhO2JBNB0UBwHSCgUAAAAAAgAAAAAEnQRMAB0ANQAAASEyFwchIgYVERQWMyEyNj0BNxUUBiMhIiY1ETQ2CQE2Mh8BFhQHAQYiLwEmND8BNjIfARYyAV4BXjxDsv6jKTs7KQH0KTvIuaX+cKW5uQHKAYsHFQdlBwf97QcVB/gHB2UHFQdvCBQETBexOyn+DCk7OylFyNulubmlAZCluf4zAYsHB2UHFQf97AcH+AcVB2UHB28HAAAAAQAKAAoEpgSmADsAAAkBNjIXARYGKwEVMzU0NhcBFhQHAQYmPQEjFTMyFgcBBiInASY2OwE1IxUUBicBJjQ3ATYWHQEzNSMiJgE+AQgIFAgBBAcFCqrICggBCAgI/vgICsiqCgUH/vwIFAj++AgFCq/ICgj++AgIAQgICsivCgUDlgEICAj++AgKyK0KBAf+/AcVB/73BwQKrcgKCP74CAgBCAgKyK0KBAcBCQcVBwEEBwQKrcgKAAEAyAAAA4QETAAZAAATMzIWFREBNhYVERQGJwERFAYrASImNRE0NvpkFR0B0A8VFQ/+MB0VZBUdHQRMHRX+SgHFDggV/BgVCA4Bxf5KFR0dFQPoFR0AAAABAAAAAASwBEwAIwAAEzMyFhURATYWFREBNhYVERQGJwERFAYnAREUBisBIiY1ETQ2MmQVHQHQDxUB0A8VFQ/+MBUP/jAdFWQVHR0ETB0V/koBxQ4IFf5KAcUOCBX8GBUIDgHF/koVCA4Bxf5KFR0dFQPoFR0AAAABAJ0AGQSwBDMAFQAAAREUBicBERQGJwEmNDcBNhYVEQE2FgSwFQ/+MBUP/hQPDwHsDxUB0A8VBBr8GBUIDgHF/koVCA4B4A4qDgHgDggV/koBxQ4IAAAAAQDIABYEMwQ2AAsAABMBFhQHAQYmNRE0NvMDLhIS/NISGRkEMv4OCx4L/g4LDhUD6BUOAAIAyABkA4QD6AAPAB8AABMzMhYVERQGKwEiJjURNDYhMzIWFREUBisBIiY1ETQ2+sgVHR0VyBUdHQGlyBUdHRXIFR0dA+gdFfzgFR0dFQMgFR0dFfzgFR0dFQMgFR0AAAEAyABkBEwD6AAPAAABERQGIyEiJjURNDYzITIWBEwdFfzgFR0dFQMgFR0DtvzgFR0dFQMgFR0dAAAAAAEAAAAZBBMEMwAVAAABETQ2FwEWFAcBBiY1EQEGJjURNDYXAfQVDwHsDw/+FA8V/jAPFRUPAmQBthUIDv4gDioO/iAOCBUBtv47DggVA+gVCA4AAAH//gACBLMETwAjAAABNzIWFRMUBiMHIiY1AwEGJjUDAQYmNQM0NhcBAzQ2FwEDNDYEGGQUHgUdFWQVHQL+MQ4VAv4yDxUFFQ8B0gIVDwHSAh0ETgEdFfwYFR0BHRUBtf46DwkVAbX+OQ4JFAPoFQkP/j4BthQJDv49AbYVHQAAAQEsAAAD6ARMABkAAAEzMhYVERQGKwEiJjURAQYmNRE0NhcBETQ2A1JkFR0dFWQVHf4wDxUVDwHQHQRMHRX8GBUdHRUBtv47DggVA+gVCA7+OwG2FR0AAAIAZADIBLAESAALABsAAAkBFgYjISImNwE2MgEhMhYdARQGIyEiJj0BNDYCrgH1DwkW++4WCQ8B9Q8q/fcD6BUdHRX8GBUdHQQ5/eQPFhYPAhwP/UgdFWQVHR0VZBUdAAEAiP/8A3UESgAFAAAJAgcJAQN1/qABYMX92AIoA4T+n/6fxgIoAiYAAAAAAQE7//wEKARKAAUAAAkBJwkBNwQo/dnGAWH+n8YCI/3ZxgFhAWHGAAIAFwAXBJkEmQAPADMAAAAyHgIUDgIiLgI0PgEFIyIGHQEjIgYdARQWOwEVFBY7ATI2PQEzMjY9ATQmKwE1NCYB4+rWm1tbm9bq1ptbW5sBfWQVHZYVHR0Vlh0VZBUdlhUdHRWWHQSZW5vW6tabW1ub1urWm7odFZYdFWQVHZYVHR0Vlh0VZBUdlhUdAAAAAAIAFwAXBJkEmQAPAB8AAAAyHgIUDgIiLgI0PgEBISIGHQEUFjMhMjY9ATQmAePq1ptbW5vW6tabW1ubAkX+DBUdHRUB9BUdHQSZW5vW6tabW1ub1urWm/5+HRVkFR0dFWQVHQACABcAFwSZBJkADwAzAAAAMh4CFA4CIi4CND4BBCIPAScmIg8BBhQfAQcGFB8BFjI/ARcWMj8BNjQvATc2NC8BAePq1ptbW5vW6tabW1ubAeUZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0JCXh4CQmNBJlbm9bq1ptbW5vW6tabrQl4eAkJjQkZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0AAgAXABcEmQSZAA8AJAAAADIeAhQOAiIuAjQ+AQEnJiIPAQYUHwEWMjcBNjQvASYiBwHj6tabW1ub1urWm1tbmwEVVAcVCIsHB/IHFQcBdwcHiwcVBwSZW5vW6tabW1ub1urWm/4xVQcHiwgUCPEICAF3BxUIiwcHAAAAAAMAFwAXBJkEmQAPADsASwAAADIeAhQOAiIuAjQ+AQUiDgMVFDsBFjc+ATMyFhUUBgciDgUHBhY7ATI+AzU0LgMTIyIGHQEUFjsBMjY9ATQmAePq1ptbW5vW6tabW1ubAT8dPEIyIRSDHgUGHR8UFw4TARkOGhITDAIBDQ6tBx4oIxgiM0Q8OpYKDw8KlgoPDwSZW5vW6tabW1ub1urWm5ELHi9PMhkFEBQQFRIXFgcIBw4UHCoZCBEQKDhcNi9IKhsJ/eMPCpYKDw8KlgoPAAADABcAFwSZBJkADwAfAD4AAAAyHgIUDgIiLgI0PgEFIyIGHQEUFjsBMjY9ATQmAyMiBh0BFBY7ARUjIgYdARQWMyEyNj0BNCYrARE0JgHj6tabW1ub1urWm1tbmwGWlgoPDwqWCg8PCvoKDw8KS0sKDw8KAV4KDw8KSw8EmVub1urWm1tbm9bq1ptWDwqWCg8PCpYKD/7UDwoyCg/IDwoyCg8PCjIKDwETCg8AAgAAAAAEsASwAC8AXwAAATMyFh0BHgEXMzIWHQEUBisBDgEHFRQGKwEiJj0BLgEnIyImPQE0NjsBPgE3NTQ2ExUUBisBIiY9AQ4BBzMyFh0BFAYrAR4BFzU0NjsBMhYdAT4BNyMiJj0BNDY7AS4BAg2WCg9nlxvCCg8PCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw+5DwqWCg9EZheoCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmBLAPCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw8KlgoPZ5cbwgoP/s2oCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmRA8KlgoPRGYAAwAXABcEmQSZAA8AGwA/AAAAMh4CFA4CIi4CND4BBCIOARQeATI+ATQmBxcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyAePq1ptbW5vW6tabW1ubAb/oxXJyxejFcnKaQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwdABxUHfHwHFQSZW5vW6tabW1ub1urWmztyxejFcnLF6MVaQAcVB3x8BxUHQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwAAAAMAFwAXBJkEmQAPABsAMAAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgcXFhQHAQYiLwEmND8BNjIfATc2MgHj6tabW1ub1urWm1tbmwG/6MVycsXoxXJyg2oHB/7ACBQIyggIagcVB0/FBxUEmVub1urWm1tbm9bq1ps7csXoxXJyxejFfWoHFQf+vwcHywcVB2oICE/FBwAAAAMAFwAXBJkEmQAPABgAIQAAADIeAhQOAiIuAjQ+AQUiDgEVFBcBJhcBFjMyPgE1NAHj6tabW1ub1urWm1tbmwFLdMVyQQJLafX9uGhzdMVyBJlbm9bq1ptbW5vW6tabO3LFdHhpAktB0P24PnLFdHMAAAAAAQAXAFMEsAP5ABUAABMBNhYVESEyFh0BFAYjIREUBicBJjQnAgoQFwImFR0dFf3aFxD99hACRgGrDQoV/t0dFcgVHf7dFQoNAasNJgAAAAABAAAAUwSZA/kAFQAACQEWFAcBBiY1ESEiJj0BNDYzIRE0NgJ/AgoQEP32EBf92hUdHRUCJhcD8f5VDSYN/lUNChUBIx0VyBUdASMVCgAAAAEAtwAABF0EmQAVAAAJARYGIyERFAYrASImNREhIiY3ATYyAqoBqw0KFf7dHRXIFR3+3RUKDQGrDSYEif32EBf92hUdHRUCJhcQAgoQAAAAAQC3ABcEXQSwABUAAAEzMhYVESEyFgcBBiInASY2MyERNDYCJsgVHQEjFQoN/lUNJg3+VQ0KFQEjHQSwHRX92hcQ/fYQEAIKEBcCJhUdAAABAAAAtwSZBF0AFwAACQEWFAcBBiY1EQ4DBz4ENxE0NgJ/AgoQEP32EBdesKWBJAUsW4fHfhcEVf5VDSYN/lUNChUBIwIkRHVNabGdcUYHAQYVCgACAAAAAASwBLAAFQArAAABITIWFREUBi8BBwYiLwEmND8BJyY2ASEiJjURNDYfATc2Mh8BFhQPARcWBgNSASwVHRUOXvkIFAhqBwf5Xg4I/iH+1BUdFQ5e+QgUCGoHB/leDggEsB0V/tQVCA5e+QcHaggUCPleDhX7UB0VASwVCA5e+QcHaggUCPleDhUAAAACAEkASQRnBGcAFQArAAABFxYUDwEXFgYjISImNRE0Nh8BNzYyASEyFhURFAYvAQcGIi8BJjQ/AScmNgP2agcH+V4OCBX+1BUdFQ5e+QgU/QwBLBUdFQ5e+QgUCGoHB/leDggEYGoIFAj5Xg4VHRUBLBUIDl75B/3xHRX+1BUIDl75BwdqCBQI+V4OFQAAAAADABcAFwSZBJkADwAfAC8AAAAyHgIUDgIiLgI0PgEFIyIGFxMeATsBMjY3EzYmAyMiBh0BFBY7ATI2PQE0JgHj6tabW1ub1urWm1tbmwGz0BQYBDoEIxQ2FCMEOgQYMZYKDw8KlgoPDwSZW5vW6tabW1ub1urWm7odFP7SFB0dFAEuFB3+DA8KlgoPDwqWCg8AAAAABQAAAAAEsASwAEkAVQBhAGgAbwAAATIWHwEWHwEWFxY3Nj8BNjc2MzIWHwEWHwIeATsBMhYdARQGKwEiBh0BIREjESE1NCYrASImPQE0NjsBMjY1ND8BNjc+BAUHBhY7ATI2LwEuAQUnJgYPAQYWOwEyNhMhIiY1ESkBERQGIyERAQQJFAUFFhbEFQ8dCAsmxBYXERUXMA0NDgQZCAEPCj0KDw8KMgoP/nDI/nAPCjIKDw8KPQsOCRkFDgIGFRYfAp2mBwQK2woKAzMDEP41sQgQAzMDCgrnCwMe/okKDwGQAlgPCv6JBLAEAgIKDXYNCxUJDRZ2DQoHIREQFRh7LAkLDwoyCg8PCq8BLP7UrwoPDwoyCg8GBQQwgBkUAwgWEQ55ogcKDgqVCgSqnQcECo8KDgr8cg8KAXf+iQoPAZAAAAAAAgAAAAwErwSmACsASQAAATYWFQYCDgQuAScmByYOAQ8BBiY1NDc+ATc+AScuAT4BNz4GFyYGBw4BDwEOBAcOARY2Nz4CNz4DNz4BBI0IGgItQmxhi2KORDg9EQQRMxuZGhYqCFUYEyADCQIQOjEnUmFch3vAJQgdHyaiPT44XHRZUhcYDhItIRmKcVtGYWtbKRYEBKYDEwiy/t3IlVgxEQgLCwwBAQIbG5kYEyJAJghKFRE8Hzdff4U/M0o1JSMbL0QJGCYvcSEhHjZST2c1ODwEJygeW0AxJUBff1UyFAABAF0AHgRyBM8ATwAAAQ4BHgQXLgc+ATceAwYHDgQHBicmNzY3PgQuAScWDgMmJy4BJyY+BDcGHgM3PgEuAicmPgMCjScfCic4R0IgBBsKGAoQAwEJEg5gikggBhANPkpTPhZINx8SBgsNJysiCRZOQQoVNU1bYC9QZwICBAUWITsoCAYdJzIYHw8YIiYHDyJJYlkEz0OAZVxEOSQMBzgXOB42IzElKRIqg5Gnl0o3Z0c6IAYWCwYNAwQFIDhHXGF1OWiqb0sdBxUknF0XNTQ8PEUiNWNROBYJDS5AQVUhVZloUSkAAAAAA//cAGoE1ARGABsAPwBRAAAAMh4FFA4FIi4FND4EBSYGFxYVFAYiJjU0NzYmBwYHDgEXHgQyPgM3NiYnJgUHDgEXFhcWNj8BNiYnJicuAQIGpJ17bk85HBw6T257naKde25POhwcOU9uewIPDwYIGbD4sBcIBw5GWg0ECxYyWl+DiINfWjIWCwQMWv3/Iw8JCSU4EC0OIw4DDywtCyIERi1JXGJcSSpJXGJcSS0tSVxiXEkqSVxiXEncDwYTOT58sLB8OzcTBg9FcxAxEiRGXkQxMEVeRSQSMRF1HiQPLxJEMA0EDyIPJQ8sSRIEAAAABP/cAAAE1ASwABQAJwA7AEwAACEjNy4ENTQ+BTMyFzczEzceARUUDgMHNz4BNzYmJyYlBgcOARceBBc3LgE1NDc2JhcHDgEXFhcWNj8CJyYnLgECUJQfW6l2WSwcOU9ue51SPUEglCYvbIknUGqYUi5NdiYLBAw2/VFGWg0ECxIqSExoNSlrjxcIB3wjDwkJJTgQLQ4MFgMsLQsieBRhdHpiGxVJXGJcSS0Pef5StVXWNBpacm5jGq0xiD8SMRFGckVzEDESHjxRQTkNmhKnbjs3EwZwJA8vEkQwDQQPC1YELEkSBAAAAAP/ngAABRIEqwALABgAKAAAJwE2FhcBFgYjISImJSE1NDY7ATIWHQEhAQczMhYPAQ4BKwEiJi8BJjZaAoIUOBQCghUbJfryJRsBCgFZDwqWCg8BWf5DaNAUGAQ6BCMUNhQjBDoEGGQEKh8FIfvgIEdEhEsKDw8KSwLT3x0U/BQdHRT8FB0AAAABAGQAFQSwBLAAKAAAADIWFREBHgEdARQGJyURFh0BFAYvAQcGJj0BNDcRBQYmPQE0NjcBETQCTHxYAWsPFhgR/plkGhPNzRMaZP6ZERgWDwFrBLBYPv6t/rsOMRQpFA0M+f75XRRAFRAJgIAJEBVAFF0BB/kMDRQpFDEOAUUBUz4AAAARAAAAAARMBLAAHQAnACsALwAzADcAOwA/AEMARwBLAE8AUwBXAFsAXwBjAAABMzIWHQEzMhYdASE1NDY7ATU0NjsBMhYdASE1NDYBERQGIyEiJjURFxUzNTMVMzUzFTM1MxUzNTMVMzUFFTM1MxUzNTMVMzUzFTM1MxUzNQUVMzUzFTM1MxUzNTMVMzUzFTM1A1JkFR0yFR37tB0VMh0VZBUdAfQdAQ8dFfwYFR1kZGRkZGRkZGRk/HxkZGRkZGRkZGT8fGRkZGRkZGRkZASwHRUyHRWWlhUdMhUdHRUyMhUd/nD9EhUdHRUC7shkZGRkZGRkZGRkyGRkZGRkZGRkZGTIZGRkZGRkZGRkZAAAAAMAAAAZBXcElwAZACUANwAAARcWFA8BBiY9ASMBISImPQE0NjsBATM1NDYBBycjIiY9ATQ2MyEBFxYUDwEGJj0BIyc3FzM1NDYEb/kPD/kOFZ/9qP7dFR0dFdECWPEV/amNetEVHR0VASMDGvkPD/kOFfG1jXqfFQSN5g4qDuYOCBWW/agdFWQVHQJYlhUI/piNeh0VZBUd/k3mDioO5g4IFZa1jXqWFQgAAAABAAAAAASwBEwAEgAAEyEyFhURFAYjIQERIyImNRE0NmQD6Ck7Oyn9rP7QZCk7OwRMOyn9qCk7/tQBLDspAlgpOwAAAAMAZAAABEwEsAAJABMAPwAAEzMyFh0BITU0NiEzMhYdASE1NDYBERQOBSIuBTURIRUUFRwBHgYyPgYmNTQ9AZbIFR3+1B0C0cgVHf7UHQEPBhgoTGacwJxmTCgYBgEsAwcNFB8nNkI2Jx8TDwUFAQSwHRX6+hUdHRX6+hUd/nD+1ClJalZcPigoPlxWakkpASz6CRIVKyclIRsWEAgJEBccISUnKhURCPoAAAAB//8A1ARMA8IABQAAAQcJAScBBEzG/p/+n8UCJwGbxwFh/p/HAicAAQAAAO4ETQPcAAUAAAkCNwkBBE392v3ZxgFhAWEDFf3ZAifH/p8BYQAAAAAC/1EAZAVfA+gAFAApAAABITIWFREzMhYPAQYiLwEmNjsBESElFxYGKwERIRchIiY1ESMiJj8BNjIBlALqFR2WFQgO5g4qDuYOCBWW/oP+HOYOCBWWAYHX/RIVHZYVCA7mDioD6B0V/dkVDvkPD/kOFQGRuPkOFf5wyB0VAiYVDvkPAAABAAYAAASeBLAAMAAAEzMyFh8BITIWBwMOASMhFyEyFhQGKwEVFAYiJj0BIRUUBiImPQEjIiYvAQMjIiY0NjheERwEJgOAGB4FZAUsIf2HMAIXFR0dFTIdKh3+1B0qHR8SHQYFyTYUHh4EsBYQoiUY/iUVK8gdKh0yFR0dFTIyFR0dFTIUCQoDwR0qHQAAAAACAAAAAASwBEwACwAPAAABFSE1MzQ2MyEyFhUFIREhBLD7UMg7KQEsKTv9RASw+1AD6GRkKTs7Kcj84AACAAAAAAXcBEwADAAQAAATAxEzNDYzITIWFSEVBQEhAcjIyDspASwqOgH0ASz+1PtQASwDIP5wAlgpOzspyGT9RAK8AAEBRQAAA2sErwAbAAABFxYGKwERMzIWDwEGIi8BJjY7AREjIiY/ATYyAnvmDggVlpYVCA7mDioO5g4IFZaWFQgO5g4qBKD5DhX9pxUO+Q8P+Q4VAlkVDvkPAAAAAQABAUQErwNrABsAAAEXFhQPAQYmPQEhFRQGLwEmND8BNhYdASE1NDYDqPkODvkPFf2oFQ/5Dg75DxUCWBUDYOUPKQ/lDwkUl5cUCQ/lDykP5Q8JFZWVFQkAAAAEAAAAAASwBLAACQAZAB0AIQAAAQMuASMhIgYHAwUhIgYdARQWMyEyNj0BNCYFNTMVMzUzFQSRrAUkFP1gFCQFrAQt/BgpOzspA+gpOzv+q2RkZAGQAtwXLSgV/R1kOylkKTs7KWQpO8hkZGRkAAAAA/+cAGQEsARMAAsAIwAxAAAAMhYVERQGIiY1ETQDJSMTFgYjIisBIiYnAj0BNDU0PgE7ASUBFSIuAz0BND4CNwRpKh0dKh1k/V0mLwMRFQUCVBQdBDcCCwzIAqP8GAQOIhoWFR0dCwRMHRX8rhUdHRUDUhX8mcj+7BAIHBUBUQ76AgQQDw36/tT6AQsTKRwyGigUDAEAAAACAEoAAARmBLAALAA1AAABMzIWDwEeARcTFzMyFhQGBw4EIyIuBC8BLgE0NjsBNxM+ATcnJjYDFjMyNw4BIiYCKV4UEgYSU3oPP3YRExwaEggeZGqfTzl0XFU+LwwLEhocExF2Pw96UxIGEyQyNDUxDDdGOASwFRMlE39N/rmtHSkoBwQLHBYSCg4REg4FBAgoKR2tAUdNfhQgExr7vgYGMT09AAEAFAAUBJwEnAAXAAABNwcXBxcHFycHJwcnBzcnNyc3Jxc3FzcDIOBO6rS06k7gLZubLeBO6rS06k7gLZubA7JO4C2bmy3gTuq0tOpO4C2bmy3gTuq0tAADAAAAZASwBLAAIQAtAD0AAAEzMhYdAQchMhYdARQHAw4BKwEiJi8BIyImNRE0PwI+ARcPAREzFzMTNSE3NQEzMhYVERQGKwEiJjURNDYCijIoPBwBSCg8He4QLBf6B0YfHz0tNxSRYA0xG2SWZIjW+v4+Mv12ZBUdHRVkFR0dBLBRLJZ9USxkLR3+qBghMhkZJCcBkCQbxMYcKGTU1f6JZAF3feGv/tQdFf4MFR0dFQH0FR0AAAAAAwAAAAAEsARMACAAMAA8AAABMzIWFxMWHQEUBiMhFh0BFAYrASImLwImNRE0NjsBNgUzMhYVERQGKwEiJjURNDYhByMRHwEzNSchNQMCWPoXLBDuHTwo/rgcPCgyGzENYJEUNy09fP3pZBUdHRVkFR0dAl+IZJZkMjIBwvoETCEY/qgdLWQsUXYHlixRKBzGxBskAZAnJGRkHRX+DBUdHRUB9BUdZP6J1dSv4X0BdwADAAAAZAUOBE8AGwA3AEcAAAElNh8BHgEPASEyFhQGKwEDDgEjISImNRE0NjcXERchEz4BOwEyNiYjISoDLgQnJj8BJwUzMhYVERQGKwEiJjURNDYBZAFrHxZuDQEMVAEuVGxuVGqDBhsP/qoHphwOOmQBJYMGGw/LFRMSFv44AgoCCQMHAwUDAQwRklb9T2QVHR0VZBUdHQNp5hAWcA0mD3lMkE7+rRUoog0CDRElCkj+CVkBUxUoMjIBAgIDBQIZFrdT5B0V/gwVHR0VAfQVHQAAAAP/nABkBLAETwAdADYARgAAAQUeBBURFAYjISImJwMjIiY0NjMhJyY2PwE2BxcWBw4FKgIjIRUzMhYXEyE3ESUFMzIWFREUBisBIiY1ETQ2AdsBbgIIFBANrAf+qg8bBoNqVW1sVAEuVQsBDW4WSpIRDAIDBQMHAwkDCgH+Jd0PHAaCASZq/qoCUGQVHR0VZBUdHQRP5gEFEBEXC/3zDaIoFQFTTpBMeQ8mDXAWrrcWGQIFAwICAWQoFf6tWQH37OQdFf4MFR0dFQH0FR0AAAADAGEAAARMBQ4AGwA3AEcAAAAyFh0BBR4BFREUBiMhIiYvAQMmPwE+AR8BETQXNTQmBhURHAMOBAcGLwEHEyE3ESUuAQMhMhYdARQGIyEiJj0BNDYB3pBOAVMVKKIN/fMRJQoJ5hAWcA0mD3nGMjIBAgIDBQIZFrdT7AH3Wf6tFSiWAfQVHR0V/gwVHR0FDm5UaoMGGw/+qgemHA4OAWsfFm4NAQxUAS5U1ssVExIW/jgCCgIJAwcDBQMBDBGSVv6tZAElgwYb/QsdFWQVHR0VZBUdAAP//QAGA+gFFAAPAC0ASQAAASEyNj0BNCYjISIGHQEUFgEVFAYiJjURBwYmLwEmNxM+BDMhMhYVERQGBwEDFzc2Fx4FHAIVERQWNj0BNDY3JREnAV4B9BUdHRX+DBUdHQEPTpBMeQ8mDXAWEOYBBRARFwsCDQ2iKBX9iexTtxYZAgUDAgIBMjIoFQFTWQRMHRVkFR0dFWQVHfzmalRubFQBLlQMAQ1uFh8BawIIEw8Mpgf+qg8bBgHP/q1WkhEMAQMFAwcDCQIKAv44FhITFcsPGwaDASVkAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBJSYGHQEhIgYdARQWMyEVFBY3JTY0AeLs1ptbW5vW7NabW1ubAob+7RAX/u0KDw8KARMXEAETEASaW5vW7NabW1ub1uzWm/453w0KFYkPCpYKD4kVCg3fDSYAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgENAQYUFwUWNj0BITI2PQE0JiMhNTQmAeLs1ptbW5vW7NabW1ubASX+7RAQARMQFwETCg8PCv7tFwSaW5vW7NabW1ub1uzWm+jfDSYN3w0KFYkPCpYKD4kVCgAAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBAyYiBwMGFjsBERQWOwEyNjURMzI2AeLs1ptbW5vW7NabW1ubAkvfDSYN3w0KFYkPCpYKD4kVCgSaW5vW7NabW1ub1uzWm/5AARMQEP7tEBf+7QoPDwoBExcAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEFIyIGFREjIgYXExYyNxM2JisBETQmAeLs1ptbW5vW7NabW1ubAZeWCg+JFQoN3w0mDd8NChWJDwSaW5vW7NabW1ub1uzWm7sPCv7tFxD+7RAQARMQFwETCg8AAAMAGAAYBJgEmAAPAJYApgAAADIeAhQOAiIuAjQ+ASUOAwcGJgcOAQcGFgcOAQcGFgcUFgcyHgEXHgIXHgI3Fg4BFx4CFxQGFBcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQYHBicmNzY3HgIXHgMfAT4CJyY+ATc+AzcmNzIWMjY3LgMnND4CJiceAT8BNi4CJwYHFB4BFS4CJz4BNxYyPgEB5OjVm1xcm9Xo1ZtcXJsBZA8rHDoKDz0PFD8DAxMBAzEFCRwGIgEMFhkHECIvCxU/OR0HFBkDDRQjEwcFaHUeISQDDTAMD0UREi4oLBAzDwQBBikEAQMLGhIXExMLBhAGKBsGBxYVEwYFAgsFAwMNFwQGCQcYFgYQCCARFwkKKiFBCwQCAQMDHzcLDAUdLDgNEiEQEgg/KhADGgMKEgoRBJhcm9Xo1ZtcXJvV6NWbEQwRBwkCAwYFBycPCxcHInIWInYcCUcYChQECA4QBAkuHgQPJioRFRscBAcSCgwCch0kPiAIAQcHEAsBAgsLIxcBMQENCQIPHxkCFBkdHB4QBgEBBwoMGBENBAMMJSAQEhYXDQ4qFBkKEhIDCQsXJxQiBgEOCQwHAQ0DBAUcJAwSCwRnETIoAwEJCwsLJQcKDBEAAAAAAQAAAAIErwSFABYAAAE2FwUXNxYGBw4BJwEGIi8BJjQ3ASY2AvSkjv79kfsGUE08hjv9rA8rD28PDwJYIk8EhVxliuh+WYcrIgsW/awQEG4PKxACV2XJAAYAAABgBLAErAAPABMAIwAnADcAOwAAEyEyFh0BFAYjISImPQE0NgUjFTMFITIWHQEUBiMhIiY9ATQ2BSEVIQUhMhYdARQGIyEiJj0BNDYFIRUhZAPoKTs7KfwYKTs7BBHIyPwYA+gpOzsp/BgpOzsEEf4MAfT8GAPoKTs7KfwYKTs7BBH+1AEsBKw7KWQpOzspZCk7ZGTIOylkKTs7KWQpO2RkyDspZCk7OylkKTtkZAAAAAIAZAAABEwEsAALABEAABMhMhYUBiMhIiY0NgERBxEBIZYDhBUdHRX8fBUdHQI7yP6iA4QEsB0qHR0qHf1E/tTIAfQB9AAAAAMAAABkBLAEsAAXABsAJQAAATMyFh0BITIWFREhNSMVIRE0NjMhNTQ2FxUzNQEVFAYjISImPQEB9MgpOwEsKTv+DMj+DDspASw7KcgB9Dsp/BgpOwSwOylkOyn+cGRkAZApO2QpO2RkZP1EyCk7OynIAAAABAAAAAAEsASwABUAKwBBAFcAABMhMhYPARcWFA8BBiIvAQcGJjURNDYpATIWFREUBi8BBwYiLwEmND8BJyY2ARcWFA8BFxYGIyEiJjURNDYfATc2MgU3NhYVERQGIyEiJj8BJyY0PwE2MhcyASwVCA5exwcHaggUCMdeDhUdAzUBLBUdFQ5exwgUCGoHB8deDgj+L2oHB8deDggV/tQVHRUOXscIFALLXg4VHRX+1BUIDl7HBwdqCBQIBLAVDl7HCBQIagcHx14OCBUBLBUdHRX+1BUIDl7HBwdqCBQIx14OFf0maggUCMdeDhUdFQEsFQgOXscHzl4OCBX+1BUdFQ5exwgUCGoHBwAAAAYAAAAABKgEqAAPABsAIwA7AEMASwAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JiQyFhQGIiY0JDIWFAYjIicHFhUUBiImNTQ2PwImNTQEMhYUBiImNCQyFhQGIiY0Advy3Z9fX5/d8t2gXl6gAcbgv29vv+C/b2/+LS0gIC0gAUwtICAWDg83ETNIMykfegEJ/octICAtIAIdLSAgLSAEqF+f3fLdoF5eoN3y3Z9Xb7/gv29vv+C/BiAtISEtICAtIQqRFxwkMzMkIDEFfgEODhekIC0gIC0gIC0gIC0AAf/YAFoEuQS8AFsAACUBNjc2JicmIyIOAwcABw4EFx4BMzI3ATYnLgEjIgcGBwEOASY0NwA3PgEzMhceARcWBgcOBgcGIyImJyY2NwE2NzYzMhceARcWBgcBDgEnLgECIgHVWwgHdl8WGSJBMD8hIP6IDx4eLRMNBQlZN0ozAiQkEAcdEhoYDRr+qw8pHA4BRyIjQS4ODyw9DQ4YIwwod26La1YOOEBGdiIwGkQB/0coW2tQSE5nDxE4Qv4eDyoQEAOtAdZbZWKbEQQUGjIhH/6JDxsdNSg3HT5CMwIkJCcQFBcMGv6uDwEcKQ4BTSIjIQEINykvYyMLKnhuiWZMBxtAOU6+RAH/SBg3ISSGV121Qv4kDwIPDyYAAAACAGQAWASvBEQAGQBEAAABPgIeAhUUDgMHLgQ1ND4CHgEFIg4DIi4DIyIGFRQeAhcWFx4EMj4DNzY3PgQ1NCYCiTB7eHVYNkN5hKg+PqeFeEM4WnZ4eQEjIT8yLSohJyktPyJDbxtBMjMPBw86KzEhDSIzKUAMBAgrKT8dF2oDtURIBS1TdkA5eYB/slVVsn+AeTlAdlMtBUgtJjY1JiY1NiZvTRc4SjQxDwcOPCouGBgwKEALBAkpKkQqMhNPbQACADn/8gR3BL4AFwAuAAAAMh8BFhUUBg8BJi8BNycBFwcvASY0NwEDNxYfARYUBwEGIi8BJjQ/ARYfAQcXAQKru0KNQjgiHR8uEl/3/nvUaRONQkIBGxJpCgmNQkL+5UK6Qo1CQjcdLhJf9wGFBL5CjUJeKmsiHTUuEl/4/nvUahKNQrpCARv+RmkICY1CukL+5UJCjUK7Qjc3LxFf+AGFAAAAAAMAyAAAA+gEsAARABUAHQAAADIeAhURFAYjISImNRE0PgEHESERACIGFBYyNjQCBqqaZDo7Kf2oKTs8Zj4CWP7/Vj09Vj0EsB4uMhX8Ryk7OykDuRUzLar9RAK8/RY9Vj09VgABAAAAAASwBLAAFgAACQEWFAYiLwEBEScBBRMBJyEBJyY0NjIDhgEbDx0qDiT+6dT+zP7oywEz0gEsAQsjDx0qBKH+5g8qHQ8j/vX+1NL+zcsBGAE01AEXJA4qHQAAAAADAScAEQQJBOAAMgBAAEsAAAEVHgQXIy4DJxEXHgQVFAYHFSM1JicuASczHgEXEScuBDU0PgI3NRkBDgMVFB4DFxYXET4ENC4CArwmRVI8LAKfBA0dMydAIjxQNyiym2SWVygZA4sFV0obLkJOMCAyVWg6HSoqFQ4TJhkZCWgWKTEiGBkzNwTgTgUTLD9pQiQuLBsH/s0NBxMtPGQ+i6oMTU8QVyhrVk1iEAFPCA4ZLzlYNkZwSCoGTf4SARIEDh02Jh0rGRQIBgPQ/soCCRYgNEM0JRkAAAABAGQAZgOUBK0ASgAAATIeARUjNC4CIyIGBwYVFB4BFxYXMxUjFgYHBgc+ATM2FjMyNxcOAyMiLgEHDgEPASc+BTc+AScjNTMmJy4CPgE3NgIxVJlemSc8OxolVBQpGxoYBgPxxQgVFS02ImIWIIwiUzUyHzY4HCAXanQmJ1YYFzcEGAcTDBEJMAwk3aYXFQcKAg4tJGEErVCLTig/IhIdFSw5GkowKgkFZDKCHj4yCg8BIh6TExcIASIfBAMaDAuRAxAFDQsRCjePR2QvORQrREFMIVgAAAACABn//wSXBLAADwAfAAABMzIWDwEGIi8BJjY7AREzBRcWBisBESMRIyImPwE2MgGQlhUIDuYOKg7mDggVlsgCF+YOCBWWyJYVCA7mDioBLBYO+g8P+g4WA4QQ+Q4V/HwDhBUO+Q8AAAQAGf//A+gEsAAHABcAGwAlAAABIzUjFSMRIQEzMhYPAQYiLwEmNjsBETMFFTM1EwczFSE1NyM1IQPoZGRkASz9qJYVCA7mDioO5g4IFZbIAZFkY8jI/tTIyAEsArxkZAH0/HwWDvoPD/oOFgOEZMjI/RL6ZJb6ZAAAAAAEABn//wPoBLAADwAZACEAJQAAATMyFg8BBiIvASY2OwERMwUHMxUhNTcjNSERIzUjFSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAljIyP7UyMgBLGRkZAEsx2QBLBYO+g8P+g4WA4SW+mSW+mT7UGRkAfRkyMgAAAAEABn//wRMBLAADwAVABsAHwAAATMyFg8BBiIvASY2OwERMwEjESM1MxMjNSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAlhkZMhkZMgBLMdkASwWDvoPD/oOFgOE/gwBkGT7UGQBkGTIyAAAAAAEABn//wRMBLAADwAVABkAHwAAATMyFg8BBiIvASY2OwERMwEjNSMRIQcVMzUDIxEjNTMBkJYVCA7mDioO5g4IFZbIArxkyAEsx2QBZGTIASwWDvoPD/oOFgOE/gxkAZBkyMj7tAGQZAAAAAAFABn//wSwBLAADwATABcAGwAfAAABMzIWDwEGIi8BJjY7AREzBSM1MxMhNSETITUhEyE1IQGQlhUIDuYOKg7mDggVlsgB9MjIZP7UASxk/nABkGT+DAH0ASwWDvoPD/oOFgOEyMj+DMj+DMj+DMgABQAZ//8EsASwAA8AEwAXABsAHwAAATMyFg8BBiIvASY2OwERMwUhNSEDITUhAyE1IQMjNTMBkJYVCA7mDioO5g4IFZbIAyD+DAH0ZP5wAZBk/tQBLGTIyAEsFg76Dw/6DhYDhMjI/gzI/gzI/gzIAAIAAAAABEwETAAPAB8AAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmAV4BkKK8u6P+cKW5uQJn/gwpOzspAfQpOzsETLuj/nClubmlAZClucg7Kf4MKTs7KQH0KTsAAAAAAwAAAAAETARMAA8AHwArAAABITIWFREUBiMhIiY1ETQ2BSEiBhURFBYzITI2NRE0JgUXFhQPAQYmNRE0NgFeAZClubml/nCju7wCZP4MKTs7KQH0KTs7/m/9ERH9EBgYBEy5pf5wpbm5pQGQo7vIOyn+DCk7OykB9Ck7gr4MJAy+DAsVAZAVCwAAAAADAAAAAARMBEwADwAfACsAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFg8BBiIvASY2AV4BkKO7uaX+cKW5uQJn/gwpOzspAfQpOzv+FQGQFQsMvgwkDL4MCwRMvKL+cKW5uaUBkKO7yDsp/gwpOzspAfQpO8gYEP0REf0QGAAAAAMAAAAABEwETAAPAB8AKwAAASEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFFxYGIyEiJj8BNjIBXgGQpbm5pf5wo7u5Amf+DCk7OykB9Ck7O/77vgwLFf5wFQsMvgwkBEy5pf5wo7u8ogGQpbnIOyn+DCk7OykB9Ck7z/0QGBgQ/REAAAAAAgAAAAAFFARMAB8ANQAAASEyFhURFAYjISImPQE0NjMhMjY1ETQmIyEiJj0BNDYHARYUBwEGJj0BIyImPQE0NjsBNTQ2AiYBkKW5uaX+cBUdHRUBwik7Oyn+PhUdHb8BRBAQ/rwQFvoVHR0V+hYETLml/nCluR0VZBUdOykB9Ck7HRVkFR3p/uQOJg7+5A4KFZYdFcgVHZYVCgAAAQDZAAID1wSeACMAAAEXFgcGAgclMhYHIggBBwYrAScmNz4BPwEhIicmNzYANjc2MwMZCQgDA5gCASwYEQ4B/vf+8wQMDgkJCQUCUCcn/tIXCAoQSwENuwUJEASeCQoRC/5TBwEjEv7K/sUFDwgLFQnlbm4TFRRWAS/TBhAAAAACAAAAAAT+BEwAHwA1AAABITIWHQEUBiMhIgYVERQWMyEyFh0BFAYjISImNRE0NgUBFhQHAQYmPQEjIiY9ATQ2OwE1NDYBXgGQFR0dFf4+KTs7KQHCFR0dFf5wpbm5AvEBRBAQ/rwQFvoVHR0V+hYETB0VZBUdOyn+DCk7HRVkFR25pQGQpbnp/uQOJg7+5A4KFZYdFcgVHZYVCgACAAAAAASwBLAAFQAxAAABITIWFREUBi8BAQYiLwEmNDcBJyY2ASMiBhURFBYzITI2PQE3ERQGIyEiJjURNDYzIQLuAZAVHRUObf7IDykPjQ8PAThtDgj+75wpOzspAfQpO8i7o/5wpbm5pQEsBLAdFf5wFQgObf7IDw+NDykPAThtDhX+1Dsp/gwpOzsplMj+1qW5uaUBkKW5AAADAA4ADgSiBKIADwAbACMAAAAyHgIUDgIiLgI0PgEEIg4BFB4BMj4BNCYEMhYUBiImNAHh7tmdXV2d2e7ZnV1dnQHD5sJxccLmwnFx/nugcnKgcgSiXZ3Z7tmdXV2d2e7ZnUdxwubCcXHC5sJzcqBycqAAAAMAAAAABEwEsAAVAB8AIwAAATMyFhURMzIWBwEGIicBJjY7ARE0NgEhMhYdASE1NDYFFTM1AcLIFR31FAoO/oEOJw3+hQ0JFfod/oUD6BUd+7QdA2dkBLAdFf6iFg/+Vg8PAaoPFgFeFR38fB0V+voVHWQyMgAAAAMAAAAABEwErAAVAB8AIwAACQEWBisBFRQGKwEiJj0BIyImNwE+AQEhMhYdASE1NDYFFTM1AkcBeg4KFfQiFsgUGPoUCw4Bfw4n/fkD6BUd+7QdA2dkBJ7+TQ8g+hQeHRX6IQ8BrxAC/H8dFfr6FR1kMjIAAwAAAAAETARLABQAHgAiAAAJATYyHwEWFAcBBiInASY0PwE2MhcDITIWHQEhNTQ2BRUzNQGMAXEHFQeLBwf98wcVB/7cBweLCBUH1APoFR37tB0DZ2QC0wFxBweLCBUH/fMICAEjCBQIiwcH/dIdFfr6FR1kMjIABAAAAAAETASbAAkAGQAjACcAABM3NjIfAQcnJjQFNzYWFQMOASMFIiY/ASc3ASEyFh0BITU0NgUVMzWHjg4qDk3UTQ4CFtIOFQIBHRX9qxUIDtCa1P49A+gVHfu0HQNnZAP/jg4OTdRMDyqa0g4IFf2pFB4BFQ7Qm9T9Oh0V+voVHWQyMgAAAAQAAAAABEwEsAAPABkAIwAnAAABBR4BFRMUBi8BByc3JyY2EwcGIi8BJjQ/AQEhMhYdASE1NDYFFTM1AV4CVxQeARUO0JvUm9IOCMNMDyoOjg4OTf76A+gVHfu0HQNnZASwAgEdFf2rFQgO0JrUmtIOFf1QTQ4Ojg4qDk3+WB0V+voVHWQyMgACAAT/7ASwBK8ABQAIAAAlCQERIQkBFQEEsP4d/sb+cQSs/TMCq2cBFP5xAacDHPz55gO5AAAAAAIAAABkBEwEsAAVABkAAAERFAYrAREhESMiJjURNDY7AREhETMHIzUzBEwdFZb9RJYVHR0V+gH0ZMhkZAPo/K4VHQGQ/nAdFQPoFB7+1AEsyMgAAAMAAABFBN0EsAAWABoALwAAAQcBJyYiDwEhESMiJjURNDY7AREhETMHIzUzARcWFAcBBiIvASY0PwE2Mh8BATYyBEwC/tVfCRkJlf7IlhUdHRX6AfRkyGRkAbBqBwf+XAgUCMoICGoHFQdPASkHFQPolf7VXwkJk/5wHRUD6BQe/tQBLMjI/c5qBxUH/lsHB8sHFQdqCAhPASkHAAMAAAANBQcEsAAWABoAPgAAAREHJy4BBwEhESMiJjURNDY7AREhETMHIzUzARcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyBExnhg8lEP72/reWFR0dFfoB9GTIZGQB9kYPD4ODDw9GDykPg4MPKQ9GDw+Dgw8PRg8pD4ODDykD6P7zZ4YPAw7+9v5wHRUD6BQe/tQBLMjI/YxGDykPg4MPKQ9GDw+Dgw8PRg8pD4ODDykPRg8Pg4MPAAADAAAAFQSXBLAAFQAZAC8AAAERISIGHQEhESMiJjURNDY7AREhETMHIzUzEzMyFh0BMzIWDwEGIi8BJjY7ATU0NgRM/qIVHf4MlhUdHRX6AfRkyGRklmQVHZYVCA7mDioO5g4IFZYdA+j+1B0Vlv5wHRUD6BQe/tQBLMjI/agdFfoVDuYODuYOFfoVHQAAAAADAAAAAASXBLAAFQAZAC8AAAERJyYiBwEhESMiJjURNDY7AREhETMHIzUzExcWBisBFRQGKwEiJj0BIyImPwE2MgRMpQ4qDv75/m6WFR0dFfoB9GTIZGTr5g4IFZYdFWQVHZYVCA7mDioD6P5wpQ8P/vf+cB0VA+gUHv7UASzIyP2F5Q8V+hQeHhT6FQ/lDwADAAAAyASwBEwACQATABcAABMhMhYdASE1NDYBERQGIyEiJjURExUhNTIETBUd+1AdBJMdFfu0FR1kAZAETB0VlpYVHf7U/doVHR0VAib+1MjIAAAGAAMAfQStBJcADwAZAB0ALQAxADsAAAEXFhQPAQYmPQEhNSE1NDYBIyImPQE0NjsBFyM1MwE3NhYdASEVIRUUBi8BJjQFIzU7AjIWHQEUBisBA6f4Dg74DhX+cAGQFf0vMhUdHRUyyGRk/oL3DhUBkP5wFQ73DwOBZGRkMxQdHRQzBI3mDioO5g4IFZbIlhUI/oUdFWQVHcjI/cvmDggVlsiWFQgO5g4qecgdFWQVHQAAAAACAGQAAASwBLAAFgBRAAABJTYWFREUBisBIiY1ES4ENRE0NiUyFh8BERQOAg8BERQGKwEiJjURLgQ1ETQ+AzMyFh8BETMRPAE+AjMyFh8BETMRND4DA14BFBklHRXIFR0EDiIaFiX+4RYZAgEVHR0LCh0VyBUdBA4iGhYBBwoTDRQZAgNkBQkVDxcZAQFkAQUJFQQxdBIUH/uuFR0dFQGNAQgbHzUeAWcfRJEZDA3+Phw/MSkLC/5BFR0dFQG/BA8uLkAcAcICBxENCxkMDf6iAV4CBxENCxkMDf6iAV4CBxENCwABAGQAAASwBEwAMwAAARUiDgMVERQWHwEVITUyNjURIREUFjMVITUyPgM1ETQmLwE1IRUiBhURIRE0JiM1BLAEDiIaFjIZGf5wSxn+DBlL/nAEDiIaFjIZGQGQSxkB9BlLBEw4AQUKFA78iBYZAQI4OA0lAYr+diUNODgBBQoUDgN4FhkBAjg4DSX+dgGKJQ04AAAABgAAAAAETARMAAwAHAAgACQAKAA0AAABITIWHQEjBTUnITchBSEyFhURFAYjISImNRE0NhcVITUBBTUlBRUhNQUVFAYjIQchJyE3MwKjAXcVHWn+2cj+cGQBd/4lASwpOzsp/tQpOzspASwCvP5wAZD8GAEsArwdFf6JZP6JZAGQyGkD6B0VlmJiyGTIOyn+DCk7OykB9Ck7ZMjI/veFo4XGyMhm+BUdZGTIAAEAEAAQBJ8EnwAmAAATNzYWHwEWBg8BHgEXNz4BHwEeAQ8BBiIuBicuBTcRohEuDosOBhF3ZvyNdxEzE8ATBxGjAw0uMUxPZWZ4O0p3RjITCwED76IRBhPCFDERdo78ZXYRBA6IDi8RogEECBUgNUNjO0qZfHNVQBAAAAACAAAAAASwBEwAIwBBAAAAMh4EHwEVFAYvAS4BPQEmIAcVFAYPAQYmPQE+BRIyHgIfARUBHgEdARQGIyEiJj0BNDY3ATU0PgIB/LimdWQ/LAkJHRTKFB2N/sKNHRTKFB0DDTE7ZnTKcFImFgEBAW0OFR0V+7QVHRUOAW0CFiYETBUhKCgiCgrIFRgDIgMiFZIYGJIVIgMiAxgVyAQNJyQrIP7kExwcCgoy/tEPMhTUFR0dFdQUMg8BLzIEDSEZAAADAAAAAASwBLAADQAdACcAAAEHIScRMxUzNTMVMzUzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYETMj9qMjIyMjIyPyuArwVHR0VDIn8SokMFR0dswRMFR37UB0CvMjIAfTIyMjI/OAdKh1kZB0qHf7UHRUyMhUdAAAAAwBkAAAEsARMAAkAEwAdAAABIyIGFREhETQmASMiBhURIRE0JgEhETQ2OwEyFhUCvGQpOwEsOwFnZCk7ASw7/Rv+1DspZCk7BEw7KfwYA+gpO/7UOyn9RAK8KTv84AGQKTs7KQAAAAAF/5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQURByMRMwcRMxHIArx8sLB8/UR8sLAYA4T+DMjI/tTIyAEsAZBkyMhkZARMsHz+DHywsHwB9HywyP1EArzIZP7UZGQBLGT+1GQB9GT+1AEsAAAABf+cAAAEsARMAA8AEwAfACUAKQAAEyEyFhURFAYjISImNRE0NhcRIREBIzUjFSMRMxUzNTMFEQcjETMHETMRyAK8fLCwfP1EfLCwGAOE/gxkZGRkZGQBkGTIyGRkBEywfP4MfLCwfAH0fLDI/UQCvP2oyMgB9MjIZP7UZAH0ZP7UASwABP+cAAAEsARMAA8AEwAbACMAABMhMhYVERQGIyEiJjURNDYXESERBSMRMxUhESEFIxEzFSERIcgCvHywsHz9RHywsBgDhP4MyMj+1AEsAZDIyP7UASwETLB8/gx8sLB8AfR8sMj9RAK8yP7UZAH0ZP7UZAH0AAAABP+cAAAEsARMAA8AEwAWABkAABMhMhYVERQGIyEiJjURNDYXESERAS0BDQERyAK8fLCwfP1EfLCwGAOE/gz+1AEsAZD+1ARMsHz+DHywsHwB9HywyP1EArz+DJaWlpYBLAAAAAX/nAAABLAETAAPABMAFwAgACkAABMhMhYVERQGIyEiJjURNDYXESERAyERIQcjIgYVFBY7AQERMzI2NTQmI8gCvHywsHz9RHywsBgDhGT9RAK8ZIImOTYpgv4Mgik2OSYETLB8/gx8sLB8AfR8sMj9RAK8/agB9GRWQUFUASz+1FRBQVYAAAAF/5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQEjESM1MwMjNTPIArx8sLB8/UR8sLAYA4T+DMjI/tTIyAEsAZBkZMjIZGQETLB8/gx8sLB8AfR8sMj9RAK8yGT+1GRkASz+DAGQZP4MZAAG/5wAAASwBEwADwATABkAHwAjACcAABMhMhYVERQGIyEiJjURNDYXESERBTMRIREzASMRIzUzBRUzNQEjNTPIArx8sLB8/UR8sLAYA4T9RMj+1GQCWGRkyP2oZAEsZGQETLB8/gx8sLB8AfR8sMj9RAK8yP5wAfT+DAGQZMjIyP7UZAAF/5wAAASwBEwADwATABwAIgAmAAATITIWFREUBiMhIiY1ETQ2FxEhEQEHIzU3NSM1IQEjESM1MwMjNTPIArx8sLB8/UR8sLAYA4T+DMdkx8gBLAGQZGTIx2RkBEywfP4MfLCwfAH0fLDI/UQCvP5wyDLIlmT+DAGQZP4MZAAAAAMACQAJBKcEpwAPABsAJQAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgchFSEVISc1NyEB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWz+1AEs/tRkZAEsBKdentvw255eXp7b8NueTHHC5MJxccLkwtDIZGTIZAAAAAAEAAkACQSnBKcADwAbACcAKwAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgcVBxcVIycjFSMRIQcVMzUB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWwyZGRklmQBLMjIBKdentvw255eXp7b8NueTHHC5MJxccLkwtBkMmQyZGQBkGRkZAAAAv/y/50EwgRBACAANgAAATIWFzYzMhYUBisBNTQmIyEiBh0BIyImNTQ2NyY1ND4BEzMyFhURMzIWDwEGIi8BJjY7ARE0NgH3brUsLC54qqp4gB0V/tQVHd5QcFZBAmKqepYKD4kVCg3fDSYN3w0KFYkPBEF3YQ6t8a36FR0dFfpzT0VrDhMSZKpi/bMPCv7tFxD0EBD0EBcBEwoPAAAAAAL/8v+cBMMEQQAcADMAAAEyFhc2MzIWFxQGBwEmIgcBIyImNTQ2NyY1ND4BExcWBisBERQGKwEiJjURIyImNzY3NjIB9m62LCsueaoBeFr+hg0lDf6DCU9xVkECYqnm3w0KFYkPCpYKD4kVCg3HGBMZBEF3YQ+teGOkHAFoEBD+k3NPRWsOExNkqWP9kuQQF/7tCg8PCgETFxDMGBMAAAABAGQAAARMBG0AGAAAJTUhATMBMwkBMwEzASEVIyIGHQEhNTQmIwK8AZD+8qr+8qr+1P7Uqv7yqv7yAZAyFR0BkB0VZGQBLAEsAU3+s/7U/tRkHRUyMhUdAAAAAAEAeQAABDcEmwAvAAABMhYXHgEVFAYHFhUUBiMiJxUyFh0BITU0NjM1BiMiJjU0Ny4BNTQ2MzIXNCY1NDYCWF6TGll7OzIJaUo3LRUd/tQdFS03SmkELzlpSgUSAqMEm3FZBoNaPWcfHRpKaR77HRUyMhUd+x5pShIUFVg1SmkCAhAFdKMAAAAGACcAFASJBJwAEQAqAEIASgBiAHsAAAEWEgIHDgEiJicmAhI3PgEyFgUiBw4BBwYWHwEWMzI3Njc2Nz4BLwEmJyYXIgcOAQcGFh8BFjMyNz4BNz4BLwEmJyYWJiIGFBYyNjciBw4BBw4BHwEWFxYzMjc+ATc2Ji8BJhciBwYHBgcOAR8BFhcWMzI3PgE3NiYvASYD8m9PT29T2dzZU29PT29T2dzZ/j0EBHmxIgQNDCQDBBcGG0dGYAsNAwkDCwccBAVQdRgEDA0iBAQWBhJROQwMAwkDCwf5Y4xjY4xjVhYGElE6CwwDCQMLBwgEBVB1GAQNDCIEjRcGG0dGYAsNAwkDCwcIBAR5sSIEDQwkAwPyb/7V/tVvU1dXU28BKwErb1NXVxwBIrF5DBYDCQEWYEZHGwMVDCMNBgSRAhh1UA0WAwkBFTpREgMVCyMMBwT6Y2OMY2MVFTpREQQVCyMMBwQCGHVQDRYDCQEkFmBGRxsDFQwjDQYEASKxeQwWAwkBAAAABQBkAAAD6ASwAAwADwAWABwAIgAAASERIzUhFSERNDYzIQEjNQMzByczNTMDISImNREFFRQGKwECvAEstP6s/oQPCgI/ASzIZKLU1KJktP51Cg8DhA8KwwMg/oTIyALzCg/+1Mj84NTUyP4MDwoBi8jDCg8AAAAABQBkAAAD6ASwAAkADAATABoAIQAAASERCQERNDYzIQEjNRMjFSM1IzcDISImPQEpARUUBisBNQK8ASz+ov3aDwoCPwEsyD6iZKLUqv6dCg8BfAIIDwqbAyD9+AFe/doERwoP/tTI/HzIyNT+ZA8KNzcKD1AAAAAAAwAAAAAEsAP0AAgAGQAfAAABIxUzFyERIzcFMzIeAhUhFSEDETM0PgIBMwMhASEEiqJkZP7UotT9EsgbGiEOASz9qMhkDiEaAnPw8PzgASwB9AMgyGQBLNTUBBErJGT+ogHCJCsRBP5w/nAB9AAAAAMAAAAABEwETAAZADIAOQAAATMyFh0BMzIWHQEUBiMhIiY9ATQ2OwE1NDYFNTIWFREUBiMhIic3ARE0NjMVFBYzITI2AQc1IzUzNQKKZBUdMhUdHRX+1BUdHRUyHQFzKTs7Kf2oARP2/ro7KVg+ASw+WP201MjIBEwdFTIdFWQVHR0VZBUdMhUd+pY7KfzgKTsE9gFGAUQpO5Y+WFj95tSiZKIAAwBkAAAEvARMABkANgA9AAABMzIWHQEzMhYdARQGIyEiJj0BNDY7ATU0NgU1MhYVESMRMxQOAiMhIiY1ETQ2MxUUFjMhMjYBBzUjNTM1AcJkFR0yFR0dFf7UFR0dFTIdAXMpO8jIDiEaG/2oKTs7KVg+ASw+WAGc1MjIBEwdFTIdFWQVHR0VZBUdMhUd+pY7Kf4M/tQkKxEEOykDICk7lj5YWP3m1KJkogAAAAP/ogAABRYE1AALABsAHwAACQEWBiMhIiY3ATYyEyMiBhcTHgE7ATI2NxM2JgMVMzUCkgJ9FyAs+wQsIBcCfRZARNAUGAQ6BCMUNhQjBDoEGODIBK37sCY3NyYEUCf+TB0U/tIUHR0UAS4UHf4MZGQAAAAACQAAAAAETARMAA8AHwAvAD8ATwBfAG8AfwCPAAABMzIWHQEUBisBIiY9ATQ2EzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBqfoKDw8K+goPDwr6Cg8PCvoKDw8BmvoKDw8K+goPD/zq+goPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K+goPD/zq+goPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K+goPDwRMDwqWCg8PCpYKD/7UDwqWCg8PCpYKDw8KlgoPDwqWCg/+1A8KlgoPDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKD/7UDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKDw8KlgoPAAAAAwAAAAAEsAUUABkAKQAzAAABMxUjFSEyFg8BBgchJi8BJjYzITUjNTM1MwEhMhYUBisBFyE3IyImNDYDITIWHQEhNTQ2ArxkZAFePjEcQiko/PwoKUIcMT4BXmRkyP4+ArwVHR0VDIn8SooNFR0dswRMFR37UB0EsMhkTzeEUzMzU4Q3T2TIZPx8HSodZGQdKh3+1B0VMjIVHQAABAAAAAAEsAUUAAUAGQArADUAAAAyFhUjNAchFhUUByEyFg8BIScmNjMhJjU0AyEyFhQGKwEVBSElNSMiJjQ2AyEyFh0BITU0NgIwUDnCPAE6EgMBSCkHIq/9WrIiCikBSAOvArwVHR0VlgET/EoBE5YVHR2zBEwVHftQHQUUOykpjSUmCBEhFpGRFiERCCb+lR0qHcjIyMgdKh39qB0VMjIVHQAEAAAAAASwBJ0ABwAUACQALgAAADIWFAYiJjQTMzIWFRQXITY1NDYzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYCDZZqapZqty4iKyf+vCcrI/7NArwVHR0VDYr8SokMFR0dswRMFR37UB0EnWqWamqW/us5Okxra0w6Of5yHSodZGQdKh3+1B0VMjIVHQAEAAAAAASwBRQADwAcACwANgAAATIeARUUBiImNTQ3FzcnNhMzMhYVFBchNjU0NjMBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYL1szb5xvIpBvoyIfLiIrJ/68Jysj/s0CvBUdHRUNivxKiQwVHR2zBEwVHftQHQUUa4s2Tm9vTj5Rj2+jGv4KOTpMa2tMOjn+ch0qHWRkHSod/tQdFTIyFR0AAAADAAAAAASwBRIAEgAiACwAAAEFFSEUHgMXIS4BNTQ+AjcBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYASz+1CU/P00T/e48PUJtj0r+ogK8FR0dFQ2K/EqJDBUdHbMETBUd+1AdBLChizlmUT9IGVO9VFShdksE/H4dKh1kZB0qHf7UHRUyMhUdAAIAyAAAA+gFFAAPACkAAAAyFh0BHgEdASE1NDY3NTQDITIWFyMVMxUjFTMVIxUzFAYjISImNRE0NgIvUjsuNv5wNi5kAZA2XBqsyMjIyMh1U/5wU3V1BRQ7KU4aXDYyMjZcGk4p/kc2LmRkZGRkU3V1UwGQU3UAAAMAZP//BEwETAAPAC8AMwAAEyEyFhURFAYjISImNRE0NgMhMhYdARQGIyEXFhQGIi8BIQcGIiY0PwEhIiY9ATQ2BQchJ5YDhBUdHRX8fBUdHQQDtgoPDwr+5eANGiUNWP30Vw0mGg3g/t8KDw8BqmQBRGQETB0V/gwVHR0VAfQVHf1EDwoyCg/gDSUbDVhYDRslDeAPCjIKD2RkZAAAAAAEAAAAAASwBEwAGQAjAC0ANwAAEyEyFh0BIzQmKwEiBhUjNCYrASIGFSM1NDYDITIWFREhETQ2ExUUBisBIiY9ASEVFAYrASImPQHIAyBTdWQ7KfopO2Q7KfopO2R1EQPoKTv7UDvxHRVkFR0D6B0VZBUdBEx1U8gpOzspKTs7KchTdf4MOyn+1AEsKTv+DDIVHR0VMjIVHR0VMgADAAEAAASpBKwADQARABsAAAkBFhQPASEBJjQ3ATYyCQMDITIWHQEhNTQ2AeACqh8fg/4f/fsgIAEnH1n+rAFWAS/+q6IDIBUd/HwdBI39VR9ZH4MCBh9ZHwEoH/5u/qoBMAFV/BsdFTIyFR0AAAAAAgCPAAAEIQSwABcALwAAAQMuASMhIgYHAwYWMyEVFBYyNj0BMzI2AyE1NDY7ATU0NjsBETMRMzIWHQEzMhYVBCG9CCcV/nAVJwi9CBMVAnEdKh19FROo/a0dFTIdFTDILxUdMhUdAocB+hMcHBP+BhMclhUdHRWWHP2MMhUdMhUdASz+1B0VMh0VAAAEAAAAAASwBLAADQAQAB8AIgAAASERFAYjIREBNTQ2MyEBIzUBIREUBiMhIiY1ETQ2MyEBIzUDhAEsDwr+if7UDwoBdwEsyP2oASwPCv12Cg8PCgF3ASzIAyD9wQoPAk8BLFQKD/7UyP4M/cEKDw8KA7YKD/7UyAAC/5wAZAUUBEcARgBWAAABMzIeAhcWFxY2NzYnJjc+ARYXFgcOASsBDgEPAQ4BKwEiJj8BBisBIicHDgErASImPwEmLwEuAT0BNDY7ATY3JyY2OwE2BSMiBh0BFBY7ATI2PQE0JgHkw0uOakkMEhEfQwoKGRMKBQ8XDCkCA1Y9Pgc4HCcDIhVkFRgDDDEqwxgpCwMiFWQVGAMaVCyfExwdFXwLLW8QBxXLdAFF+goPDwr6Cg8PBEdBa4pJDgYKISAiJRsQCAYIDCw9P1c3fCbqFB0dFEYOCEAUHR0UnUplNQcmFTIVHVdPXw4TZV8PCjIKDw8KMgoPAAb/nP/mBRQEfgAJACQANAA8AFIAYgAAASU2Fh8BFgYPASUzMhYfASEyFh0BFAYHBQYmJyYjISImPQE0NhcjIgYdARQ7ATI2NTQmJyYEIgYUFjI2NAE3PgEeARceAT8BFxYGDwEGJi8BJjYlBwYfAR4BPwE2Jy4BJy4BAoEBpxMuDiAOAxCL/CtqQ0geZgM3FR0cE/0fFyIJKjr+1D5YWLlQExIqhhALIAsSAYBALS1ALf4PmBIgHhMQHC0aPzANITNQL3wpgigJASlmHyElDR0RPRMFAhQHCxADhPcICxAmDyoNeMgiNtQdFTIVJgeEBBQPQ1g+yD5YrBwVODMQEAtEERzJLUAtLUD+24ITChESEyMgAwWzPUkrRSgJL5cvfRxYGyYrDwkLNRAhFEgJDAQAAAAAAwBkAAAEOQSwAFEAYABvAAABMzIWHQEeARcWDgIPATIeBRUUDgUjFRQGKwEiJj0BIxUUBisBIiY9ASMiJj0BNDY7AREjIiY9ATQ2OwE1NDY7ATIWHQEzNTQ2AxUhMj4CNTc0LgMjARUhMj4CNTc0LgMjAnGWCg9PaAEBIC4uEBEGEjQwOiodFyI2LUAjGg8KlgoPZA8KlgoPrwoPDwpLSwoPDwqvDwqWCg9kD9cBBxwpEwsBAQsTKRz++QFrHCkTCwEBCxMpHASwDwptIW1KLk0tHwYGAw8UKDJOLTtdPCoVCwJLCg8PCktLCg8PCksPCpYKDwJYDwqWCg9LCg8PCktLCg/+1MgVHR0LCgQOIhoW/nDIFR0dCwoEDiIaFgAAAwAEAAIEsASuABcAKQAsAAATITIWFREUBg8BDgEjISImJy4CNRE0NgQiDgQPARchNy4FAyMT1AMMVnokEhIdgVL9xFKCHAgYKHoCIIx9VkcrHQYGnAIwnAIIIClJVSGdwwSuelb+YDO3QkJXd3ZYHFrFMwGgVnqZFyYtLSUMDPPzBQ8sKDEj/sIBBQACAMgAAAOEBRQADwAZAAABMzIWFREUBiMhIiY1ETQ2ARUUBisBIiY9AQHblmesVCn+PilUrAFINhWWFTYFFKxn/gwpVFQpAfRnrPwY4RU2NhXhAAACAMgAAAOEBRQADwAZAAABMxQWMxEUBiMhIiY1ETQ2ARUUBisBIiY9AQHbYLOWVCn+PilUrAFINhWWFTYFFJaz/kIpVFQpAfRnrPwY4RU2NhXhAAACAAAAFAUOBBoAFAAaAAAJASUHFRcVJwc1NzU0Jj4CPwEnCQEFJTUFJQUO/YL+hk5klpZkAQEBBQQvkwKCAVz+ov6iAV4BXgL//uWqPOCWx5SVyJb6BA0GCgYDKEEBG/1ipqaTpaUAAAMAZAH0BLADIAAHAA8AFwAAEjIWFAYiJjQkMhYUBiImNCQyFhQGIiY0vHxYWHxYAeh8WFh8WAHofFhYfFgDIFh8WFh8WFh8WFh8WFh8WFh8AAAAAAMBkAAAArwETAAHAA8AFwAAADIWFAYiJjQSMhYUBiImNBIyFhQGIiY0Aeh8WFh8WFh8WFh8WFh8WFh8WARMWHxYWHz+yFh8WFh8/shYfFhYfAAAAAMAZABkBEwETAAPAB8ALwAAEyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2fQO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PBEwPCpYKDw8KlgoP/nAPCpYKDw8KlgoP/nAPCpYKDw8KlgoPAAAABAAAAAAEsASwAA8AHwAvADMAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFhURFAYjISImNRE0NhcVITUBXgH0ory7o/4Mpbm5Asv9qCk7OykCWCk7O/2xAfQVHR0V/gwVHR1HAZAEsLuj/gylubmlAfSlucg7Kf2oKTs7KQJYKTtkHRX+1BUdHRUBLBUdZMjIAAAAAAEAZABkBLAETAA7AAATITIWFAYrARUzMhYUBisBFTMyFhQGKwEVMzIWFAYjISImNDY7ATUjIiY0NjsBNSMiJjQ2OwE1IyImNDaWA+gVHR0VMjIVHR0VMjIVHR0VMjIVHR0V/BgVHR0VMjIVHR0VMjIVHR0VMjIVHR0ETB0qHcgdKh3IHSodyB0qHR0qHcgdKh3IHSodyB0qHQAAAAYBLAAFA+gEowAHAA0AEwAZAB8AKgAAAR4BBgcuATYBMhYVIiYlFAYjNDYBMhYVIiYlFAYjNDYDFRQGIiY9ARYzMgKKVz8/V1c/P/75fLB8sAK8sHyw/cB8sHywArywfLCwHSodKAMRBKNDsrJCQrKy/sCwfLB8fLB8sP7UsHywfHywfLD+05AVHR0VjgQAAAH/tQDIBJQDgQBCAAABNzYXAR4BBw4BKwEyFRQOBCsBIhE0NyYiBxYVECsBIi4DNTQzIyImJyY2NwE2HwEeAQ4BLwEHIScHBi4BNgLpRRkUASoLCAYFGg8I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<h1 class="title toc-ignore">Narratives of Backlash? Perceptions of
Changing Status Hierarchies in Open-Ended Survey Responses</h1>
<h3 class="subtitle">Replication file</h3>
<h4 class="author">Magdalena Breyer, Tabea Palmtag, Delia Zollinger</h4>
<h4 class="date">2025-09-16</h4>

</div>

<div id="TOC">
<ul>
<li><a href="#data" id="toc-data">Data</a>
<ul>
<li><a href="#dictionary" id="toc-dictionary">Dictionary</a></li>
<li><a href="#pre-processing-text" id="toc-pre-processing-text">Pre-processing text</a></li>
</ul></li>
<li><a href="#figure-2" id="toc-figure-2">Figure 2</a></li>
<li><a href="#figure-3" id="toc-figure-3">Figure 3</a></li>
<li><a href="#table-1" id="toc-table-1">Table 1</a></li>
<li><a href="#figure-4" id="toc-figure-4">Figure 4</a></li>
<li><a href="#figure-5" id="toc-figure-5">Figure 5</a></li>
<li><a href="#figure-6" id="toc-figure-6">Figure 6</a></li>
<li><a href="#appendix" id="toc-appendix">Appendix</a>
<ul>
<li><a href="#table-a1" id="toc-table-a1">Table A1</a></li>
<li><a href="#figure-a1" id="toc-figure-a1">Figure A1</a></li>
<li><a href="#figure-a2" id="toc-figure-a2">Figure A2</a></li>
<li><a href="#figure-a3" id="toc-figure-a3">Figure A3</a></li>
<li><a href="#figure-a4" id="toc-figure-a4">Figure A4</a></li>
<li><a href="#figure-c1" id="toc-figure-c1">Figure C1</a></li>
<li><a href="#table-c1" id="toc-table-c1">Table C1</a></li>
<li><a href="#table-c2" id="toc-table-c2">Table C2</a></li>
<li><a href="#figure-c2" id="toc-figure-c2">Figure C2</a></li>
<li><a href="#figure-c3" id="toc-figure-c3">Figure C3</a></li>
<li><a href="#figure-c4" id="toc-figure-c4">Figure C4</a></li>
<li><a href="#figure-c5" id="toc-figure-c5">Figure C5</a></li>
<li><a href="#figure-c6" id="toc-figure-c6">Figure C6</a></li>
<li><a href="#table-c3" id="toc-table-c3">Table C3</a></li>
<li><a href="#table-c4" id="toc-table-c4">Table C4</a></li>
<li><a href="#table-c5" id="toc-table-c5">Table C5</a></li>
<li><a href="#table-c6" id="toc-table-c6">Table C6</a></li>
<li><a href="#table-c7" id="toc-table-c7">Table C7</a></li>
<li><a href="#figure-c7" id="toc-figure-c7">Figure C7</a></li>
<li><a href="#table-c9" id="toc-table-c9">Table C9</a></li>
</ul></li>
</ul>
</div>

<p>Packages</p>
<pre class="r"><code>knitr::opts_chunk$set(echo = TRUE, warning = F, message = F)

# packages
if (!require(&quot;pacman&quot;)) install.packages(&quot;pacman&quot;) 

pacman::p_load(
  ggpp,                # CRAN v0.5.8-1
  ggthemes,            # CRAN v5.1.0
  gridExtra,           # CRAN v2.3
  kableExtra,          # CRAN v1.4.0
  marginaleffects,     # CRAN 0.28.0
  modelsummary,        # CRAN 2.3.0
  quanteda,            # CRAN v4.3.0
  quanteda.textmodels, # CRAN v0.9.10
  quanteda.textplots,  # CRAN v0.95
  quanteda.textstats,  # CRAN v0.97.2
  tidyverse            # CRAN v2.0.0
)


theme_set(theme_minimal())</code></pre>
<div id="data" class="section level1">
<h1>Data</h1>
<pre class="r"><code>df_p &lt;- readRDS(&quot;../data/df_pfinal.RDS&quot;)</code></pre>
<pre class="r"><code># Terms to delete from open answers

delete &lt;- c(
  &quot;`&quot;   ,
  &quot;30&quot;  ,
  &quot;ahnung&quot;  ,
  &quot;allgemein&quot;, 
  &quot;anerkannt&quot;   ,
  &quot;anerkennung&quot; ,
  &quot;befinden&quot;,
  &quot;beispielsweise&quot;  ,
  &quot;bekommen&quot;    ,
  &quot;bereiche&quot;    ,
  &quot;besser&quot;  ,
  &quot;bzw&quot; ,
  &quot;dabei&quot;,
  &quot;dafür&quot;   ,
  &quot;darunter&quot;,
  &quot;davon&quot;,
  &quot;denen&quot;   ,
  &quot;denke&quot;   ,
  &quot;denken&quot;,
  &quot;desto&quot;,
  &quot;doch&quot;,
  &quot;dh&quot;,
  &quot;d.h&quot;,
  &quot;eher&quot;    ,
  &quot;eigentlich&quot;  ,
  &quot;eigenschaften&quot;,
  &quot;eindeutig&quot;   ,
  &quot;einmal&quot;  ,
  &quot;erfahren&quot;    ,
  &quot;erhalten&quot;    ,
  &quot;etc&quot; ,
  &quot;evtl&quot;,
  &quot;finde&quot;,
  &quot;früher&quot;  ,
  &quot;frage&quot;,
  &quot;ganzen&quot;,
  &quot;gar&quot; ,
  &quot;geht&quot;    ,
  &quot;gerade&quot;,
  &quot;gesellschaft&quot;    ,
  &quot;gestellt&quot;    ,
  &quot;gewinnen&quot;    ,
  &quot;gewonnen&quot;    ,
  &quot;geworden&quot;    ,
  &quot;gibt&quot;    ,
  &quot;gilt&quot;    ,
  &quot;große&quot;   ,
  &quot;großer&quot;  ,
  &quot;großen&quot;,
  &quot;gruppe&quot;  ,
  &quot;gruppen&quot; ,
  &quot;gut&quot; ,
  &quot;gute&quot;    ,
  &quot;gutes&quot;   ,
  &quot;habe&quot;    ,
  &quot;halten&quot;  ,
  &quot;heute&quot;   ,
  &quot;heutigen&quot;    ,
  &quot;heutzutage&quot;, 
  &quot;hohe&quot;    ,
  &quot;hohen&quot;   ,
  &quot;hoher&quot;   ,
  &quot;höher&quot;   ,
  &quot;ihre&quot;    ,
  &quot;ihrem&quot;   ,
  &quot;ihren&quot;   ,
  &quot;ihrer&quot;   ,
  &quot;ja&quot;,
  &quot;je&quot;,
  &quot;jedoch&quot;,
  &quot;jemand&quot;,
  &quot;jahre&quot;,
  &quot;jahren&quot;  ,
  &quot;jahrzehnte&quot;,
  &quot;jetzt&quot;,
  &quot;k.a&quot;,
  &quot;kommen&quot;  ,
  &quot;kommt&quot;   ,
  &quot;können&quot;  ,
  &quot;lassen&quot;  ,
  &quot;laufen&quot;  ,
  &quot;leben&quot;   ,
  &quot;lebensweisen&quot;,
  &quot;legen&quot;,
  &quot;leute&quot;   ,
  &quot;mal&quot; ,
  &quot;man&quot; ,
  &quot;manchmal&quot;    ,
  &quot;mehr&quot;    ,
  &quot;meinung&quot; ,
  &quot;meinungen&quot;,
  &quot;meist&quot;   ,
  &quot;menschen&quot;    ,
  &quot;mich&quot;    ,
  &quot;müssen&quot;  ,
  &quot;niemals&quot; ,
  &quot;obwohl&quot;  ,
  &quot;oftmals&quot; ,
  &quot;personen&quot;    ,
  &quot;position&quot;, 
  &quot;positionen&quot;,
  &quot;respekt&quot; ,
  &quot;respektiert&quot;,
  &quot;schon&quot;   ,
  &quot;sehe&quot;,
  &quot;sehen&quot;,
  &quot;sei&quot;,
  &quot;sicherlich&quot;  ,
  &quot;siehe_oben&quot;  ,
  &quot;sowie&quot;   ,
  &quot;stark&quot;,
  &quot;stehen&quot;  ,
  &quot;steht&quot;   ,
  &quot;stellen&quot; ,
  &quot;teil&quot;,
  &quot;teils&quot;   ,
  &quot;teilweise&quot;   ,
  &quot;tendenziell&quot; ,
  &quot;unserer&quot;,
  &quot;usw&quot; ,
  &quot;u&quot;,
  &quot;und_co&quot;,
  &quot;u_ä&quot;,
  &quot;u_u&quot;,
  &quot;überhaupt&quot;,
  &quot;verbessert&quot;,
  &quot;verfügen&quot;    ,
  &quot;verlieren&quot;   ,
  &quot;verliert&quot;,
  &quot;verloren&quot;    ,
  &quot;viel&quot;    ,
  &quot;vielen&quot;,
  &quot;weiß&quot;    ,
  &quot;weiss&quot;,
  &quot;wenig&quot;   ,
  &quot;weniger&quot; ,
  &quot;will&quot;    ,
  &quot;wohl&quot;    ,
  &quot;wollen&quot;  ,
  &quot;wurde&quot;   ,
  &quot;wurden&quot;  ,
  &quot;z.b&quot; ,
  &quot;zb&quot;,
  &quot;z_b&quot;
)

# Terms to compound (per question)

comp_winning &lt;- c(&quot;viel geld&quot;, &quot;sozial* berufe&quot;, &quot;sozial* medien&quot;, &quot;social media*&quot;, &quot;soziale* bereiche*&quot;, &quot;soziale* netzwerke*&quot;,
                  &quot;soziale* herkunft&quot;,
                  &quot;gut* *bildung&quot;,&quot;besser* bildung&quot;, &quot;gute* einkommen&quot;, &quot;gute* hause&quot;,
                  &quot;weiß nicht&quot;, &quot;weiß ich nicht&quot;, &quot;weiß es nicht&quot;,
                  &quot;kein* ahnung&quot;, &quot;kann ich nicht sagen&quot;,
                  &quot;nicht die geringste ahnung&quot;, &quot;kene ahnung&quot;,
                  &quot;weis* nicht&quot;, &quot;fällt dazu nichts&quot;, &quot;fällt mir&quot;,
                  &quot;fällt nicht*&quot;, &quot;fällt nix&quot;,
                  &quot;keine angabe&quot;, &quot;keine angaben&quot;, &quot;z b&quot;,
                  &quot;u u&quot;, &quot;u ä&quot;)

comp_losing &lt;- c(&quot;einfache* arbeiter&quot;, &quot;einfache* leute&quot;, &quot;wenig geld&quot;, &quot;soziale* berufe*&quot;, 
                 &quot;sozial schwache*&quot;,&quot;hart arbeitende*&quot;, &quot;har*z 4&quot;, &quot;hartz iv&quot;, &quot;handwerklich* berufe*&quot;, 
                 &quot;normale* arbeiter&quot;, &quot;viel geld&quot;, &quot;nicht viel geld&quot;, &quot;mal so viel geld&quot;, &quot;oben herab&quot;, &quot;öffentliche* dienst&quot;,
                 &quot;weiß nicht&quot;, &quot;kein* ahnung&quot;, &quot;kann ich nicht sagen&quot;,
                 &quot;nicht die geringste ahnung&quot;, &quot;kene ahnung&quot;,
                 &quot;weis* nicht&quot;, &quot;fällt dazu nichts&quot;, &quot;fällt mir&quot;,
                 &quot;fällt nicht*&quot;, &quot;fällt nix&quot;, &quot;siehe oben&quot;,
                 &quot;keine angabe&quot;, &quot;keine angaben&quot;, &quot;z b&quot;,
                 &quot;u u&quot;, &quot;u ä&quot;)

comp_high &lt;- c(&quot;viel geld&quot;, &quot;mehr geld&quot;, &quot;sich * leisten&quot;,
               &quot;hohe* einkommen&quot;, &quot;reich geboren&quot;, &quot;keine sorgen&quot;,
               &quot;gut* einkommen&quot;, &quot;was besseres&quot;, &quot;gut* *bildung&quot;,
               &quot;finanziell *gesichert&quot;, &quot;finanziell unabhängig&quot;,
               &quot;über leichen&quot;, &quot;vitamin b&quot;, &quot;höhere* dienst*&quot;,&quot;öffentliche* dienst&quot;,
               &quot;eigene* vorteil&quot;,&quot;auf kosten&quot;,
               &quot;weiß nicht&quot;, &quot;kein* ahnung&quot;, &quot;kann ich nicht sagen&quot;,
               &quot;nicht die geringste ahnung&quot;, &quot;kene ahnung&quot;,
               &quot;weis* nicht&quot;, &quot;fällt dazu nichts&quot;, &quot;fällt mir&quot;,
               &quot;fällt nicht*&quot;, &quot;fällt nix&quot;, &quot;keine angabe&quot;, &quot;keine angaben&quot;, &quot;z b&quot;,
               &quot;u u&quot;, &quot;u ä&quot;)

comp_medium &lt;- c(&quot;wie ich&quot;, &quot;gut* *bildung&quot;, &quot;leisten können&quot;, &quot;nach oben&quot;,
                 &quot;gerade so&quot;, &quot;mittler* einkommen&quot;, &quot;gut* einkommen&quot;,
                 &quot;runden kommen&quot;, &quot;sozial engagiert&quot;, &quot;hart arbeitend*&quot;,
                 &quot;genug geld&quot;, &quot;mit dem strom&quot;, &quot;eigene meinung&quot;, &quot;9 to 5&quot;, &quot;klein unternehmer&quot;,
                 &quot;öffentliche* dienst&quot;,&quot;mittlerer dienst&quot;, &quot;höhere* dienst&quot;,
                 &quot;weiß nicht&quot;, &quot;kein* ahnung&quot;, &quot;kann ich nicht sagen&quot;,
                 &quot;nicht die geringste ahnung&quot;, &quot;kene ahnung&quot;,
                 &quot;weis* nicht&quot;, &quot;fällt dazu nichts&quot;, &quot;fällt mir&quot;,
                 &quot;fällt nicht*&quot;, &quot;fällt nix&quot;,&quot;keine angabe&quot;, &quot;keine angaben&quot;, &quot;z b&quot;,
                 &quot;u u&quot;, &quot;u ä&quot;)

comp_low &lt;- c(&quot;wenig geld&quot;, &quot;einfache* arbeiter&quot;, &quot;wenig geld&quot;, &quot;soziale* berufe&quot;, &quot;sozial schwache*&quot;, &quot;weiße männer&quot;,
              &quot;hart arbeitende*&quot;, &quot;ältere* menschen&quot;, &quot;har*z 4&quot;, &quot;hartz iv&quot;, &quot;handwerklich* berufe&quot;, 
              &quot;viel geld&quot;, &quot;nicht viel geld&quot;, &quot;mal so viel geld&quot;, &quot;ohne ausbildung&quot;, &quot;gering* einkommen&quot;, 
              &quot;keine arbeit&quot;, &quot;runden kommen&quot;, &quot;nicht bereit&quot;, &quot;alg i&quot;, &quot;alg ii&quot;, &quot;arbeitslosengeld ii&quot;, &quot;arbeitslosengeld i und ii&quot;,
              &quot;gering verdiener&quot;, &quot;viele kinder&quot;, &quot;schlechte bildung&quot;, &quot;schlecht bezahlt*&quot;, &quot;keine *bildung&quot;,
              &quot;mangelnde bildung&quot;, &quot;geringe* bildung&quot;,
              &quot;nicht arbeiten&quot;, &quot;in den mund&quot;, &quot;auf der straße&quot;, &quot;ohne ausbildung&quot;, &quot;nicht arbeiten&quot;, 
              &quot;keine arbeit&quot;, &quot;ohne arbeit&quot;, &quot;auf kosten&quot;,
              &quot;weiß nicht&quot;, &quot;kein* ahnung&quot;, &quot;kann ich nicht sagen&quot;,
              &quot;nicht die geringste ahnung&quot;, &quot;kene ahnung&quot;,
              &quot;weis* nicht&quot;, &quot;fällt dazu nichts&quot;, &quot;fällt mir&quot;,
              &quot;fällt nicht*&quot;, &quot;fällt nix&quot;, &quot;keine angabe&quot;, &quot;keine angaben&quot;, &quot;z b&quot;,
              &quot;u u&quot;, &quot;u ä&quot;)</code></pre>
<div id="dictionary" class="section level2">
<h2>Dictionary</h2>
<pre class="r"><code>dict_groups &lt;- dictionary(list(
  no_content = list(
    dont_know = c(&quot;weiß_nicht&quot;, &quot;weis_nicht&quot;, &quot;weiss_nicht&quot;, &quot;weiss_ich_nicht&quot;, &quot;weiß_ich_nicht&quot;, &quot;weis_ich_nicht&quot;, &quot;weiß_es_nicht&quot;,#compound
                  &quot;keine_ahnung&quot;, &quot;keine_richtige_ahnung&quot;, &quot;nicht_die_geringste_ahnung&quot;,&quot;kene_ahnung&quot;,
                  &quot;kann_ich_nicht_sagen&quot;, &quot;fällt_dazu_nichts&quot;, &quot;fällt_mir&quot;,
                  &quot;fällt_nicht&quot;, &quot;fallt_nichts&quot;, &quot;fällt_nix&quot;, &quot;keine_angabe&quot;, &quot;keine_angaben&quot;,
                  &quot;ka&quot;, &quot;k.a&quot;)
  ),
  socioeconomic = list(
    occ = list(
      elite = c(&quot;ärzte&quot;, &quot;manager&quot;, &quot;unternehmer&quot;, &quot;wissenschaftler&quot;, &quot;banker&quot;, &quot;bänker&quot;, &quot;banken&quot;,
                &quot;geschäftsführer&quot;, &quot;selbstständige&quot;, &quot;selbstständig&quot;, &quot;chefs&quot;, &quot;vorstände&quot;, &quot;wirtschaftsbosse&quot;,
                &quot;juristen&quot;, &quot;anwälte&quot;, &quot;firmen&quot;, &quot;steuerberater&quot;, &quot;management&quot;, &quot;geschäftsleute&quot;,
                &quot;firmenchefs&quot;, &quot;geschäftsmänner&quot;, &quot;professoren&quot;, &quot;lobbyisten&quot;, &quot;journalisten&quot;),
      state = list(
        police = c(&quot;polizei&quot;, &quot;polizisten&quot;, &quot;feuerwehr&quot;, &quot;polizeibeamte&quot;, &quot;soldaten&quot;),
        other_state = c(&quot;beamte&quot;, &quot;lehrer&quot;, &quot;öffentlichen_dienst&quot;, &quot;öffentlicher_dienst&quot;,
                        &quot;höheren_dienst&quot;, &quot;höherer_dienst&quot;, &quot;mittlerer_dienst&quot;, &quot;mittleren_dienst&quot;, &quot;dienst&quot;)
      ),
      social = c(&quot;soziale_berufe&quot;, &quot;sozialen_berufen&quot;, &quot;pfleger&quot;, &quot;pflegerinnen&quot;, &quot;pflegepersonal&quot;, &quot;pflegende&quot;,
                 &quot;pflegekräfte&quot;, &quot;pflege&quot;, &quot;medizinisches&quot;, &quot;medizinischen&quot;, &quot;krankenhäusern&quot;, &quot;krankenhäuser&quot;,
                 &quot;krankenschwester&quot;, &quot;krankenschwestern&quot;, &quot;erzieher&quot;, &quot;altenpfleger&quot;, &quot;sozialen_bereich&quot;,
                 &quot;pflegeberufe&quot;, &quot;pflegeberufen&quot;, &quot;gesundheitswesen&quot;, &quot;rettungsdienst&quot;, &quot;mediziner&quot;,
                 &quot;sozialarbeiter&quot;, &quot;sanitäter&quot;),
      production = c(&quot;handwerker&quot;, &quot;handwerk&quot;, &quot;handwerksberufe&quot;, &quot;handwerkliche&quot;, &quot;handwerkliche_berufe&quot;, &quot;handwerklichen_berufen&quot;,
                     &quot;arbeiter&quot;, &quot;landwirte&quot;, &quot;facharbeiter&quot;, &quot;meister&quot;, &quot;bäcker&quot;),
      tech = c(&quot;it&quot;, &quot;digital&quot;, &quot;internet&quot;, &quot;informatiker&quot;, &quot;technik&quot;),
      service = c(&quot;verkäufer&quot;, &quot;einzelhandel&quot;, &quot;niedriglohnsektor&quot;, &quot;dienstleister&quot;,
                  &quot;reinigungskräfte&quot;),
      generic = c(&quot;angestellte&quot;, &quot;arbeitende&quot;, &quot;arbeit&quot;, &quot;beruf&quot;, &quot;berufe&quot;, &quot;berufen&quot;, &quot;berufliche&quot;,
                  &quot;job&quot;, &quot;jobs&quot;, &quot;arbeitnehmer&quot;, &quot;arbeiten&quot;, &quot;normale_arbeiter&quot;, &quot;normalen_arbeiter&quot;, &quot;fachkräfte&quot;,
                  &quot;hart_arbeitende&quot;, &quot;hart_arbeitend&quot;, &quot;klein_unternehmer&quot;, &quot;kleinunternehmer&quot;, &quot;erwerbstätige&quot;, &quot;arbeitsmarkt&quot;,
                  &quot;berufstätige&quot;, &quot;berufstätigen&quot;, &quot;personal&quot;, &quot;beschäftigte&quot;, &quot;beschäftigten&quot;,
                  &quot;mitarbeiter&quot;, &quot;steuerzahler&quot;, &quot;9_to_5&quot;, &quot;gearbeitet&quot;, &quot;angestellt&quot;, &quot;wirtschaft&quot;),
      welfare = c(&quot;arbeitslose&quot;, &quot;arbeitslos&quot;, &quot;arbeitslosigkeit&quot;, &quot;nicht_arbeiten&quot;, &quot;keine_arbeit&quot;, &quot;ohne_arbeit&quot;,
                  &quot;alg_i&quot;, &quot;alg_ii&quot;, &quot;arbeitslosengeld_ii&quot;, &quot;arbeitslosengeld&quot;,
                  &quot;rente&quot;, &quot;langzeitarbeitslose&quot;),
      new = list(
        social_media = c( &quot;influencer&quot;, &quot;influenzer&quot;, &quot;sozialen_medien&quot;,
                          &quot;soziale_medien&quot;, &quot;social_media&quot;),
        sports = c(&quot;sportler&quot;, &quot;profisportler&quot;, &quot;fußballer&quot;, &quot;fussballer&quot;, &quot;profifußballer&quot;,
                   &quot;profi_sportler&quot;, &quot;profi_fußballer&quot;, &quot;profi_fussballer&quot;, &quot;formel_1&quot;), 
        stars = c(&quot;promis&quot;, &quot;prominente&quot;, &quot;stars&quot;, &quot;berühmte&quot;, &quot;sänger&quot;)
      )), 
    wealth = list(
      rich = c(&quot;reiche&quot;, &quot;reich&quot;, &quot;reichen&quot;, &quot;viel_geld&quot;, &quot;wohlhabende&quot;, &quot;reichtum&quot;,
               &quot;wohlhabend&quot;, &quot;millionäre&quot;, &quot;erben&quot;, &quot;geerbt&quot;, &quot;vermögen&quot;, &quot;vermögend&quot;, &quot;luxus&quot;,
               &quot;hohes_einkommen&quot;, &quot;wohlstand&quot;, &quot;unternehmen&quot;, &quot;superreiche&quot;, &quot;superreichen&quot;, &quot;autos&quot;,
               &quot;großverdiener&quot;, &quot;gutverdienende&quot;, &quot;gutes_einkommen&quot;, &quot;bonzen&quot;, &quot;obere_zehntausend&quot;,
               &quot;oberen_zehntausend&quot;, &quot;vielverdiener&quot;, 
               &quot;finanziell_gesichert&quot;, &quot;finanziell_abgesichert&quot;, &quot;finanziell_unabhängig&quot;
      ),
      poor = c(&quot;obdachlose&quot;, &quot;arme&quot;, &quot;geringverdiener&quot;, &quot;gering_verdiener&quot;, &quot;arm&quot;, &quot;armut&quot;, &quot;ärmer&quot;, &quot;wenig_geld&quot;,
               &quot;empfänger&quot;, &quot;hartz_4&quot;, &quot;hartz4&quot;, &quot;hartz_iv&quot;, &quot;harz_4&quot;,&quot;harz_iv&quot;, &quot;sozial_schwache&quot;,
               &quot;sozialhilfeempfänger&quot;, &quot;sozialleistungen&quot;, &quot;sparen&quot;, &quot;runden_kommen&quot;, &quot;in_den_mund&quot;,
               &quot;schmarotzer&quot;, &quot;sozialschmarotzer&quot;, &quot;geringem_einkommen&quot;, &quot;geringes_einkommen&quot;, &quot;amt&quot;,
               &quot;schlecht_bezahlte&quot;, &quot;prekäre&quot;, &quot;existenzminimum&quot;), 
      generic = c(&quot;einkommen&quot;, &quot;lohn&quot;, &quot;gehalt&quot;, &quot;geld&quot;, &quot;besitz&quot;, &quot;besitzen&quot;, &quot;finanziell&quot;, 
                  &quot;finanziellen&quot;, &quot;finanzielles&quot;, &quot;steuern&quot;, &quot;eigentum&quot;, &quot;verdiener&quot;,
                  &quot;urlaub&quot;, &quot;reisen&quot;, &quot;haus&quot;, &quot;knete&quot;, &quot;auto&quot;, &quot;verdienende&quot;, &quot;miete&quot;,
                  &quot;bezahlt&quot;, &quot;bezahlen&quot;, &quot;verdienst&quot;, &quot;wirtschaftlich&quot;, &quot;lebensunterhalt&quot;)
    )),
  education = list(
    high = c(&quot;gebildete&quot;, &quot;gebildet&quot;, &quot;studiert&quot;, &quot;studierte&quot;, &quot;studierten&quot;, &quot;akademiker&quot;,
             &quot;guter_ausbildung&quot;, &quot;gute_ausbildung&quot;, &quot;gute_bildung&quot;, &quot;studieren&quot;, 
             &quot;bessere bildung&quot;, &quot;besserer_bildung&quot;, &quot;studenten&quot;,
             &quot;abitur&quot;, &quot;studierende&quot;, &quot;bildungsbürgertum&quot;, &quot;studium&quot;),
    low = c(&quot;bildungsfern&quot;, &quot;ungelernt&quot;, &quot;ungelernte&quot;, &quot;ungebildet&quot;, &quot;ungebildete&quot;,
            &quot;schlechte_bildung&quot;, &quot;keine_bildung&quot;, &quot;mangelnde_bildung&quot;, &quot;geringe_bildung&quot;,
            &quot;ohne_ausbildung&quot;),
    generic = c(&quot;bildung&quot;, &quot;ausbildung&quot;, &quot;weiterbilden&quot;, &quot;lernen&quot;, &quot;bildungsgrad&quot;,
                &quot;schulbildung&quot;, &quot;bildungsniveau&quot;, &quot;ausbildungsberufe&quot;, &quot;ausgebildete&quot;,
                &quot;bildungsstand&quot;, &quot;qualifikation&quot;, &quot;bildungschancen&quot;, &quot;bildungsabschluss&quot;,
                &quot;schulabschluss&quot;, &quot;schule&quot;, &quot;abschluss&quot;)
  ),
  sociocultural = list(
    gender = c(&quot;frauen&quot;, &quot;männer&quot;, &quot;geschlecht&quot;, &quot;frau&quot;, &quot;mann&quot;, &quot;feministen&quot;, &quot;feministinnen&quot;, 
               &quot;mütter&quot;, 
               &quot;weisse_männer&quot;, &quot;weiße_männer&quot; 
    ), 
    sexuality = c(&quot;homosexuelle&quot;, &quot;homo&quot;, &quot;lgbtq&quot;, &quot;lgbtqia&quot;, &quot;lgbt&quot;, &quot;lgbtx&quot;, &quot;schwule&quot;, &quot;schwul&quot;, &quot;lesben&quot;, &quot;lesbisch&quot;, 
                  &quot;queere&quot;, &quot;queer&quot;, &quot;gay&quot;,
                  &quot;heterosexuelle&quot;, 
                  &quot;transgender&quot;, &quot;transsexuell&quot;, &quot;transsexuelle&quot;, &quot;trans menschen&quot;),
    race = c(&quot;migranten&quot;, &quot;migrationshintergrund&quot;, &quot;ausländer&quot;, &quot;zuwanderer&quot;,
             &quot;flüchtlinge&quot;, &quot;kanacken&quot;, &quot;einwanderer&quot;, &quot;integration&quot;, &quot;geflüchtete&quot;, &quot;asylanten&quot;,
             &quot;deutsche&quot;, &quot;deutschen&quot;, &quot;weiße&quot;, &quot;deutschland&quot;, &quot;volk&quot;, &quot;einheimische&quot;, &quot;weisse&quot;,
             &quot;herkunft&quot;, &quot;integrieren&quot;, &quot;bipoc&quot;, &quot;bpoc&quot;, &quot;poc&quot;),
    marginalized = c(&quot;minderheiten&quot;, &quot;randgruppen&quot;, &quot;rand&quot;)
  ),
  attribute = list(
    personality = list(
      negative = c(&quot;arrogant&quot;, &quot;arroganz&quot;, &quot;arrogante&quot;, &quot;egoistisch&quot;, &quot;egoisten&quot;, &quot;schlecht&quot;, &quot;schlechte&quot;, &quot;schlechter&quot;,
                   &quot;abgehoben&quot;, &quot;überheblich&quot;, &quot;egozentrisch&quot;, &quot;oberflächlich&quot;, &quot;skrupellos&quot;,
                   &quot;faul&quot;, &quot;faule&quot;,&quot;lügner&quot;, &quot;schwache&quot;, &quot;über_leichen&quot;, &quot;korrupt&quot;, &quot;gier&quot;,
                   &quot;betrüger&quot;, &quot;auf_kosten&quot;, &quot;nicht_bereit&quot;, &quot;abhängig&quot;, &quot;angeber&quot;),
      neutral = c(&quot;normale&quot;, &quot;normal&quot;, &quot;einfach&quot;, &quot;einfache&quot;, &quot;einfachen&quot;, &quot;normalen&quot;, &quot;wie_ich&quot;,
                  &quot;neues&quot;, &quot;neue&quot;, &quot;neu&quot;, &quot;alleine&quot;, &quot;unauffällig&quot;, &quot;durchschnitt&quot;, &quot;durchschnittlichem&quot;,
                  &quot;durchschnittliches&quot;, &quot;sorgen&quot;, &quot;klein&quot;, &quot;kleine&quot;),
      positive = c(&quot;sozial&quot;, &quot;soziale&quot;, &quot;offen&quot;, &quot;weltoffen&quot;, &quot;zielstrebig&quot;, &quot;fleißig&quot;, &quot;fleißige&quot;,
                   &quot;zufrieden&quot;, &quot;bodenständig&quot;, &quot;engagiert&quot;, &quot;engagement&quot;, &quot;engagieren&quot;, &quot;ehrgeizig&quot;, &quot;glücklich&quot;,
                   &quot;hilfsbereit&quot;, &quot;interessiert&quot;, &quot;unabhängig&quot;, &quot;aktiv&quot;, &quot;arbeitssam&quot;,
                   &quot;wichtig&quot;, &quot;wichtigen&quot;, &quot;wichtige&quot;, &quot;gemeinwohl&quot;, &quot;systemrelevant&quot;, &quot;systemrelevante&quot;,
                   &quot;freundlich&quot;, &quot;lieb&quot;, &quot;ehrlich&quot;)
    ),
    actions = list(
      effort = c(&quot;leisten&quot;, &quot;leistungen&quot;, &quot;verdienen&quot;, &quot;verdient&quot;, &quot;versuchen&quot;, 
                 &quot;nach_oben&quot;, &quot;kämpfen&quot;, &quot;mut&quot;, &quot;erreichen&quot;, &quot;mühe&quot;,
                 &quot;erfolg&quot;, &quot;erfolgreich&quot;, &quot;erledigen&quot;, &quot;leistungsträger&quot;, &quot;bemühen&quot;,
                 &quot;mehrwert&quot;),
      luck = c(&quot;glück&quot;, &quot;vorteil&quot;, &quot;chancen&quot;, &quot;chance&quot;),
      community = c(&quot;teilen&quot;, &quot;kümmern&quot;, &quot;dienen&quot;, &quot;sozialen&quot;, &quot;für_andere&quot;,
                    &quot;einsetzen&quot;),
      misfortune = c(&quot;pech&quot;, &quot;geraten&quot;),
      cognitive = c(&quot;wissen&quot;), 
      middle = c(&quot;mittelschicht&quot;, &quot;mittelstand&quot;, &quot;mitte&quot;, &quot;mittleren&quot;, &quot;mittelständler&quot;,
                 &quot;mittelklasse&quot;)
    )),
  politics = list(
    power = c(&quot;politik&quot;, &quot;macht&quot;, &quot;politiker&quot;, &quot;parteien&quot;, &quot;regierung&quot;, &quot;politisch&quot;,
              &quot;politische&quot;, &quot;politischen&quot;, &quot;lobbyisten&quot;, &quot;lobby&quot;),
    groups = c(&quot;querdenker&quot;, &quot;konservativ&quot;, &quot;konservative&quot;, &quot;rechtsradikale&quot;,
               &quot;nazis&quot;, &quot;schwurbler&quot;, &quot;grüne&quot;, &quot;grünen&quot;, &quot;aktivisten&quot;, &quot;rechte&quot;, &quot;weltverbesserer&quot;,
               &quot;linke&quot;),
    environment = c(&quot;umwelt&quot;, &quot;umweltaktivisten&quot;, &quot;umweltschützer&quot;, &quot;veganer&quot;,
                    &quot;klimaaktivisten&quot;)
  )
))</code></pre>
</div>
<div id="pre-processing-text" class="section level2">
<h2>Pre-processing text</h2>
<div id="winning-groups" class="section level4">
<h4>Winning groups</h4>
<pre class="r"><code># 1. remove short answers

df_p &lt;- df_p %&gt;% 
  mutate(nchar_win = nchar(rg_winning)) 

df_p_keepwin &lt;- filter(df_p, nchar_win &gt;=3)

# 2. corpus, tokenization, lower case

corp_rgwin &lt;- corpus(df_p_keepwin, text_field = &quot;rg_winning&quot;)

toks_win &lt;- tokens(corp_rgwin, remove_punct = TRUE, remove_symbols = TRUE, padding = FALSE) %&gt;% 
  tokens_tolower()

# 3. compound those expressions that have meaning (see above)

toks_win_comp &lt;- tokens_compound(toks_win, pattern = phrase(comp_winning)) 

# 4. stopwords and other terms to delete

toks_win_comp &lt;- toks_win_comp %&gt;% 
  tokens_remove(pattern = c(stopwords(&quot;de&quot;, source = 
                        &quot;marimo&quot;), delete), 
                valuetype = &quot;fixed&quot;, padding = TRUE)

# 5. text matrix and delete white space

dfmat_win &lt;- toks_win_comp %&gt;% 
  dfm() %&gt;% 
  dfm_trim(max_termfreq = 5000, termfreq_type = &quot;count&quot;) # to remove white space (or tab)</code></pre>
</div>
<div id="losing-groups" class="section level4">
<h4>Losing groups</h4>
<pre class="r"><code># 1. remove short answers

df_p &lt;- df_p %&gt;% 
  mutate(nchar_lose = nchar(rg_losing)) 

df_p_keeplose &lt;- filter(df_p, nchar_lose &gt;=3)

# 2. corpus, tokenization, lower case

corp_rglose &lt;- corpus(df_p_keeplose, text_field = &quot;rg_losing&quot;)

# tokens

toks_lose &lt;- tokens(corp_rglose, remove_punct = TRUE, remove_symbols = T, padding = F) %&gt;% 
  tokens_tolower()

# 3. compound those expressions that have meaning

toks_lose_comp &lt;- tokens_compound(toks_lose, pattern = phrase(comp_losing))

# 4. stopwords and other terms to delete

toks_lose_comp &lt;- toks_lose_comp %&gt;% 
  tokens_remove(pattern = c(stopwords(&quot;de&quot;, source = 
                        &quot;marimo&quot;), 
                        delete
                        ), 
                valuetype = &quot;fixed&quot;, padding = TRUE)

# 5. text matrix and delete white space

dfmat_lose &lt;- toks_lose_comp %&gt;% 
  dfm() %&gt;% 
  dfm_trim(max_termfreq = 5000, termfreq_type = &quot;count&quot;) # to remove white space (or tab)</code></pre>
</div>
<div id="high-groups" class="section level4">
<h4>High groups</h4>
<pre class="r"><code># 1. remove short answers

df_p &lt;- df_p %&gt;% 
  mutate(nchar_high = nchar(rg_high)) 

df_p_keephigh &lt;- filter(df_p, nchar_high &gt;=3)

# 2. corpus, tokenization, lower case

corp_rghigh &lt;- corpus(df_p_keephigh, text_field = &quot;rg_high&quot;)

# tokens

toks_high &lt;- tokens(corp_rghigh, remove_punct = TRUE, remove_symbols = T, padding = F) %&gt;% 
  tokens_tolower()

# 3. compound those expressions that have meaning:

toks_high_comp &lt;- tokens_compound(toks_high, pattern = phrase(comp_high))

# 4. stopwords and other terms to delete

toks_high_comp &lt;- toks_high_comp %&gt;% 
  tokens_remove(pattern = c(stopwords(&quot;de&quot;, source = 
                        &quot;marimo&quot;), delete, &quot;rand&quot;, &quot;oben&quot;, &quot;oberen&quot;), 
                valuetype = &quot;fixed&quot;, padding = TRUE)

# 5. text matrix and delete white space

dfmat_high &lt;- toks_high_comp %&gt;% 
  dfm() %&gt;% 
  dfm_trim(max_termfreq = 5000, termfreq_type = &quot;count&quot;) # to remove white space (or tab)</code></pre>
</div>
<div id="medium-groups" class="section level4">
<h4>Medium groups</h4>
<pre class="r"><code># 1. remove short answers

df_p &lt;- df_p %&gt;% 
  mutate(nchar_med = nchar(rg_med)) 

df_p_keepmed &lt;- filter(df_p, nchar_med &gt;=3)

# 2. corpus, tokenization, lower case

corp_rgmed &lt;- corpus(df_p_keepmed, text_field = &quot;rg_med&quot;)

# tokens

toks_med &lt;- tokens(corp_rgmed, remove_punct = TRUE, remove_symbols = TRUE, padding = F) %&gt;% 
  tokens_tolower()

# 3. compound

toks_med_comp &lt;- tokens_compound(toks_med, pattern = phrase(comp_medium))

# 4. stopwords and other terms to delete

toks_med_comp &lt;- toks_med_comp %&gt;% 
  tokens_remove(pattern = c(stopwords(&quot;de&quot;, source = 
                        &quot;marimo&quot;), delete, &quot;mitte&quot;, &quot;mittlere&quot;, &quot;5&quot;), 
                valuetype = &quot;fixed&quot;, padding = TRUE)

# 5. text matrix and delete white space

dfmat_med &lt;- toks_med_comp %&gt;% 
  dfm() %&gt;% 
  dfm_trim(max_termfreq = 5000, termfreq_type = &quot;count&quot;) # to remove white space (or tab)</code></pre>
</div>
<div id="low-groups" class="section level4">
<h4>Low groups</h4>
<pre class="r"><code># 1. remove short answers

df_p &lt;- df_p %&gt;% 
  mutate(nchar_low = nchar(rg_low)) 

df_p_keeplow &lt;- filter(df_p, nchar_low &gt;=3)

# 2. corpus, tokenization, lower case

corp_rglow &lt;- corpus(df_p_keeplow, text_field = &quot;rg_low&quot;)

# tokens

toks_low &lt;- tokens(corp_rglow, remove_punct = TRUE, remove_symbols = TRUE, padding = F) %&gt;% 
  tokens_tolower()

# 3. compound

toks_low_comp &lt;- tokens_compound(toks_low, pattern = phrase(comp_low))

# 4. stopwords and other terms to delete

toks_low_comp &lt;- toks_low_comp %&gt;% 
  tokens_remove(pattern = c(stopwords(&quot;de&quot;, source = 
                        &quot;marimo&quot;), delete, &quot;rand&quot;, &quot;unteren&quot;, &quot;oberen&quot;), 
                valuetype = &quot;fixed&quot;, padding = TRUE)

# 5. text matrix and delete white space

dfmat_low &lt;- toks_low_comp %&gt;% 
  dfm() %&gt;% 
  dfm_trim(max_termfreq = 5000, termfreq_type = &quot;count&quot;) # to remove white space (or tab)</code></pre>
</div>
</div>
</div>
<div id="figure-2" class="section level1">
<h1>Figure 2</h1>
<pre class="r"><code># for plots: dictionary as df and then merge to textstat_frequency object

dict_df &lt;- dict_groups %&gt;% 
  unlist() %&gt;% 
  tibble(dimension = gsub(&quot;[0-9]&quot;, &quot;&quot;, names(.)),
         feature = .) %&gt;% 
  separate(col =  dimension, into = c(&quot;dimension&quot;, &quot;subdimension&quot;), &quot;\\.&quot;) %&gt;% 
  filter(dimension %in% c(&quot;socioeconomic&quot;, &quot;sociocultural&quot;, &quot;education&quot;)) %&gt;% 
  mutate(
    dimension = fct_relevel(factor(dimension), &quot;socioeconomic&quot;, &quot;education&quot;, &quot;sociocultural&quot;)
  )

# HIGH

high_dim &lt;- textstat_frequency(dfmat_high, n = 20)

translations_high &lt;- c(&quot;wealth&quot;, &quot;wealth&quot;, &quot;the wealthy&quot;, &quot;earn&quot;, &quot;aloof&quot;, &quot;influence&quot;, &quot;academics&quot;, &quot;to work&quot;, &quot;wealthy&quot;, &quot;egoistic&quot;, &quot;arrogant&quot;, &quot;power&quot;, &quot;doctors&quot;, &quot;entrepreneurs&quot;,  &quot;managers&quot;, &quot;rich&quot;, &quot;a lot of money&quot;, &quot;money&quot;, &quot;the rich&quot;, &quot;politicians&quot;)

high_dim &lt;- dict_df %&gt;% 
  left_join(high_dim, ., by = c(&quot;feature&quot;))

plot_high_f &lt;- high_dim %&gt;%  
  ggplot(aes(x = reorder(feature, frequency), y = frequency, fill = dimension)) +
  geom_col(width = 0.7, size = 0.2, color = &quot;black&quot;) +
  coord_flip() +
  scale_y_continuous(limits = c(0, 410)) +
  scale_fill_manual(values = c(&quot;gray1&quot;, &quot;gray40&quot;, &quot;gray72&quot;),
                    na.value = &quot;gray99&quot;,
                    name = NULL,
                    na.translate = F) +
    annotate(geom = &quot;text&quot;, x = 1:20, y = 410, size = 2.5, color = &quot;gray25&quot;, 
             label = translations_high, hjust = 1)+
  labs(x = NULL, y = &quot;Frequency&quot;, subtitle = &quot;Static: high&quot;)+
  theme(legend.position = &quot;bottom&quot;,
        axis.text.y = element_text(size = 8))

# LOW

low_dim &lt;- textstat_frequency(dfmat_low, n = 20)

translations_low &lt;- c(&quot;lazy&quot;, &quot;work&quot;, &quot;low-income earners&quot;, &quot;education&quot;, &quot;income&quot;, &quot;state&quot;, &quot;poor&quot;, &quot;the sick&quot;, &quot;welfare recipients&quot;, &quot;hartz 4&quot;, &quot;money&quot;, &quot;to work&quot;, &quot;single parents&quot;, &quot;recipients&quot;, &quot;the poor&quot;, &quot;migrants&quot;, &quot;workers&quot;, &quot;homeless&quot;, &quot;pensioners&quot;, &quot;the unemployed&quot;)

low_dim &lt;- dict_df %&gt;% 
  left_join(low_dim, ., by = c(&quot;feature&quot;))

plot_low_f &lt;- low_dim %&gt;%  
  ggplot(aes(x = reorder(feature, frequency), y = frequency, fill = dimension)) +
  geom_col(width = 0.7,  size = 0.2, color = &quot;black&quot;) +
  coord_flip() +
  scale_fill_manual(values = c(&quot;gray1&quot;, &quot;gray40&quot;, &quot;gray72&quot;),
                    na.value = &quot;gray99&quot;,
                    name = NULL,
                    na.translate = F) +
    annotate(geom = &quot;text&quot;, x = 1:20, y = 460, size = 2.5, color = &quot;gray25&quot;, 
             label = translations_low, hjust = 1)+
  labs(x = NULL, y = &quot;Frequency&quot;, subtitle = &quot;Static: low&quot;)+
  theme(legend.position = &quot;bottom&quot;,
         axis.text.y = element_text(size = 8))

plots_freq_static &lt;- arrangeGrob(plot_high_f, plot_low_f,
                          nrow = 2, heights = unit(c(9, 9), c(&quot;cm&quot;))
                         )

grid.arrange(plots_freq_static)</code></pre>
<p><img role="img" 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" width="768" /></p>
</div>
<div id="figure-3" class="section level1">
<h1>Figure 3</h1>
<pre class="r"><code># Winning

win_dim &lt;-  textstat_frequency(dfmat_win, n = 20)

translations_win &lt;- c(&quot;perform&quot;, &quot;education&quot;, &quot;occupations&quot;, &quot;children&quot;, &quot;time&quot;, &quot;graduates&quot;, &quot;don&#39;t know&quot;, &quot;work&quot;, &quot;migrants&quot;, &quot;politicians&quot;, &quot;academics&quot;, &quot;no idea&quot;, &quot;homosexuals&quot;, &quot;doctors&quot;, &quot;influencers&quot;, &quot;to work&quot;, &quot;the rich&quot;, &quot;money&quot;, &quot;craftsmen&quot;, &quot;women&quot;)

win_dim &lt;- dict_df %&gt;% 
  left_join(win_dim, ., by = c(&quot;feature&quot;))

plot_win_f &lt;- win_dim %&gt;% 
  ggplot(aes(x = reorder(feature, frequency), y = frequency, fill = dimension)) +
  geom_col(width = 0.7, size = 0.2, color = &quot;black&quot;) +
  coord_flip() +
  scale_y_continuous(limits = c(0, 290)) +
  scale_fill_manual(values = c(&quot;gray1&quot;, &quot;gray40&quot;, &quot;gray72&quot;),
                    na.value = &quot;gray99&quot;,
                    name = NULL,
                    na.translate = F) +
  annotate(geom = &quot;text&quot;, x = 1:20, y = 290, size = 2.5, color = &quot;gray25&quot;, 
           label = translations_win, hjust = 1)+
  labs(x = NULL, y = &quot;Frequency&quot;, subtitle = &quot;Dynamic: winning&quot;) +
  theme(legend.position = &quot;bottom&quot;,
         axis.text.y = element_text(size = 8))

# Losing

lose_dim &lt;-  textstat_frequency(dfmat_lose, n = 20)

translations_lose &lt;- c(&quot;money&quot;, &quot;no idea&quot;, &quot;men&quot;, &quot;migrants&quot;, &quot;civil servants&quot;, &quot;doctors&quot;, &quot;occupations&quot;, &quot;middle class&quot;, &quot;don&#39;t know&quot;, &quot;the elderly&quot;, &quot;the unemployed&quot;, &quot;pensioners&quot;, &quot;policemen&quot;, &quot;work&quot;, &quot;workers&quot;, &quot;to work&quot;, &quot;police&quot;, &quot;teachers&quot;, &quot;craftsmen&quot;, &quot;politicians&quot;)

lose_dim &lt;- dict_df %&gt;% 
  left_join(lose_dim, ., by = c(&quot;feature&quot;))

plot_lose_f &lt;- lose_dim %&gt;% 
  ggplot(aes(x = reorder(feature, frequency), y = frequency, fill = dimension)) +
  geom_col(width = 0.7, size = 0.2, color = &quot;black&quot;) +
  coord_flip() +
  scale_fill_manual(values = c(&quot;gray1&quot;, #&quot;gray40&quot;, 
                               &quot;gray72&quot;),
                    na.value = &quot;gray99&quot;,
                    name = NULL,
                    na.translate = F) +
  annotate(geom = &quot;text&quot;, x = 1:20, y = 400, size = 2.5, color = &quot;gray25&quot;, 
           label = translations_lose, hjust = 1)+
  labs(x = NULL, y = &quot;Frequency&quot;, subtitle = &quot;Dynamic: losing&quot;) +
  theme(legend.position = &quot;bottom&quot;,
         axis.text.y = element_text(size = 8))

plots_freq_dyn &lt;- arrangeGrob(plot_win_f, plot_lose_f,
                          nrow = 2, heights = unit(c(9, 9), c(&quot;cm&quot;)))

grid.arrange(plots_freq_dyn)</code></pre>
<p><img role="img" 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" width="768" /></p>
</div>
<div id="table-1" class="section level1">
<h1>Table 1</h1>
<pre class="r"><code># Apply dictionary

df_win_dict &lt;- tokens_lookup(toks_win_comp, dict_groups, valuetype = &quot;glob&quot;) %&gt;% 
  dfm() %&gt;% 
  convert(to = &quot;data.frame&quot;) %&gt;% 
  bind_cols(docvars(toks_win_comp, &quot;ResponseId&quot;)) %&gt;% 
  rename(ResponseId = last_col()) %&gt;% 
  select(-doc_id) %&gt;% 
  mutate(Question = &quot;dynamic&quot;,
         Type = &quot;winning&quot;)

df_lose_dict &lt;- tokens_lookup(toks_lose_comp, dict_groups, valuetype = &quot;glob&quot;) %&gt;% 
  dfm() %&gt;% 
  convert(to = &quot;data.frame&quot;) %&gt;%
  bind_cols(docvars(toks_lose_comp, &quot;ResponseId&quot;)) %&gt;% 
  rename(ResponseId = last_col()) %&gt;% 
  select(-doc_id) %&gt;% 
  mutate(Question = &quot;dynamic&quot;,
         Type = &quot;losing&quot;)

df_high_dict &lt;- tokens_lookup(toks_high_comp, dict_groups, valuetype = &quot;glob&quot;) %&gt;% 
  dfm() %&gt;% 
  convert(to = &quot;data.frame&quot;) %&gt;% 
  mutate(Question = &quot;static&quot;)

df_med_dict &lt;- tokens_lookup(toks_med_comp, dict_groups, valuetype = &quot;glob&quot;) %&gt;% 
  dfm() %&gt;% 
  convert(to = &quot;data.frame&quot;) %&gt;% 
  mutate(Question = &quot;static&quot;)

df_low_dict &lt;- tokens_lookup(toks_low_comp, dict_groups, valuetype = &quot;glob&quot;) %&gt;% 
  dfm() %&gt;% 
  convert(to = &quot;data.frame&quot;) %&gt;% 
  mutate(Question = &quot;static&quot;)

df_dyn_stat &lt;- bind_rows(df_win_dict, df_lose_dict, df_high_dict, df_med_dict, df_low_dict) %&gt;% 
  rowwise() %&gt;% 
  mutate(
    sum_eco = sum(c_across(starts_with(&quot;socioeconomic&quot;)), na.rm = T), # sum per answer,
    sum_cult = sum(c_across(starts_with(&quot;sociocultural&quot;)), na.rm = T), # sum per answer,
    sum_edu = sum(c_across(starts_with(&quot;education&quot;)), na.rm = T), # sum per answer
    sum_pol = sum(c_across(starts_with(&quot;politics&quot;)), na.rm = T), # sum per answer,
    sum_attr = sum(c_across(starts_with(&quot;attribute&quot;)), na.rm = T), # sum per answer
    sum_dk = sum(c_across(starts_with(&quot;no_content&quot;)), na.rm = T),
    eco = if_else(sum_eco &gt; 0, 1, 0), # binary: 1 is eco. mentioned, 0 not
    cult = if_else(sum_cult &gt; 0, 1, 0), # binary: 1 is cult. mentioned, 0 not
    edu = if_else(sum_edu &gt; 0, 1, 0), # binary: 1 is edu. mentioned, 0 not
    pol = if_else(sum_pol &gt; 0, 1, 0),
    attr = if_else(sum_attr &gt; 0 , 1, 0),
    dk = if_else(sum_dk &gt; 0, 1, 0)
    ) %&gt;% 
  ungroup()

# Table

df_dyn_stat %&gt;% 
  group_by(Question) %&gt;% 
  summarise( 
          &quot;Socioeconomic.N&quot; = sum(eco == 1),
          &quot;Socioeconomic.%&quot; = mean(eco) * 100,
          &quot;Sociocultural.N&quot; = sum(cult == 1),
          &quot;Sociocultural.%&quot; = mean(cult) * 100,
          &quot;Education.N&quot; = sum(edu == 1),
          &quot;Education.%&quot; = mean(edu) * 100,
          &quot;Total.N&quot; = n(),
          &quot;Total.%&quot; = 100,
          ) %&gt;% 
  ungroup() %&gt;% 
  pivot_longer(cols  = -Question, names_to = c(&quot;Dimension&quot;, &quot;.value&quot;), 
    names_sep = &quot;\\.&quot;) %&gt;% 
  arrange(fct_relevel(factor(Dimension), 
                            &quot;Socioeconomic&quot;, &quot;Sociocultural&quot;, &quot;Education&quot;,
                           &quot;Total&quot;
                      ), 
          desc(Question)) %&gt;% 
  relocate(Dimension) %&gt;% 
  kbl(caption = &quot;Salience of dictionary dimensions (percentage of answers mentioning the dimension by question type)&quot;,
      booktabs = T,
      digits = 1,
      #format = &quot;latex&quot;
      ) %&gt;% 
  collapse_rows(columns = 1:2, latex_hline = &quot;major&quot;) %&gt;% 
  kable_styling()</code></pre>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>
Salience of dictionary dimensions (percentage of answers mentioning the
dimension by question type)
</caption>
<thead>
<tr>
<th style="text-align:left;">
Dimension
</th>
<th style="text-align:left;">
Question
</th>
<th style="text-align:right;">
N
</th>
<th style="text-align:right;">
%
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;vertical-align: middle !important;" rowspan="2">
Socioeconomic
</td>
<td style="text-align:left;">
static
</td>
<td style="text-align:right;">
5183
</td>
<td style="text-align:right;">
47.2
</td>
</tr>
<tr>
<td style="text-align:left;">
dynamic
</td>
<td style="text-align:right;">
2820
</td>
<td style="text-align:right;">
38.4
</td>
</tr>
<tr>
<td style="text-align:left;vertical-align: middle !important;" rowspan="2">
Sociocultural
</td>
<td style="text-align:left;">
static
</td>
<td style="text-align:right;">
529
</td>
<td style="text-align:right;">
4.8
</td>
</tr>
<tr>
<td style="text-align:left;">
dynamic
</td>
<td style="text-align:right;">
847
</td>
<td style="text-align:right;">
11.5
</td>
</tr>
<tr>
<td style="text-align:left;vertical-align: middle !important;" rowspan="2">
Education
</td>
<td style="text-align:left;">
static
</td>
<td style="text-align:right;">
940
</td>
<td style="text-align:right;">
8.6
</td>
</tr>
<tr>
<td style="text-align:left;">
dynamic
</td>
<td style="text-align:right;">
647
</td>
<td style="text-align:right;">
8.8
</td>
</tr>
<tr>
<td style="text-align:left;vertical-align: middle !important;" rowspan="2">
Total
</td>
<td style="text-align:left;">
static
</td>
<td style="text-align:right;">
10987
</td>
<td style="text-align:right;">
100.0
</td>
</tr>
<tr>
<td style="text-align:left;">
dynamic
</td>
<td style="text-align:right;">
7349
</td>
<td style="text-align:right;">
100.0
</td>
</tr>
</tbody>
</table>
</div>
<div id="figure-4" class="section level1">
<h1>Figure 4</h1>
<pre class="r"><code>#translations
translations_winatt &lt;- c(&quot;politicians&quot;, &quot;the rich&quot;, &quot;don&#39;t care&quot;, &quot;earnings&quot;, &quot;top&quot;, &quot;don&#39;t know&quot;, &quot;immigrants&quot;, &quot;Greens&quot;, &quot;no idea&quot;, &quot;don&#39;t know&quot;, &quot;to care&quot;, &quot;income&quot;, &quot;worries&quot;, &quot;arrogant&quot;, &quot;wealth&quot;, &quot;foreigners&quot;, &quot;cars&quot;, &quot;proud&quot;, &quot;worked&quot;, &quot;asylum seekers&quot;, &quot;celebrities&quot;, &quot;skilled workers&quot;, &quot;computer scientists&quot;, &quot;own&quot;, &quot;scientists&quot;, &quot;brought&quot;, &quot;social occupations&quot;, &quot;community&quot;, &quot;gender&quot;, &quot;open&quot;, &quot;disability&quot;, &quot;gender inclusive form&quot;, &quot;migration background&quot;, &quot;occupations&quot;, &quot;cosmopolitan&quot;, &quot;influencers&quot;, &quot;social&quot;, &quot;environmentalists&quot;, &quot;LGBTQ&quot;, &quot;women&quot;)

# compute keyness and merge to dictionary to categorize terms
tstat_key_win_att &lt;- textstat_keyness(dfm_group(dfmat_win, groups = dfmat_win$galtan_group),
                                  target = &quot;progressive&quot;) %&gt;% 
  left_join(dict_df, by = c(&quot;feature&quot;)) %&gt;% 
  mutate(
    targetgroup = if_else(chi2 &gt;= 0, &quot;target&quot;, &quot;reference&quot;),
    chi_abs = abs(chi2) 
  ) %&gt;% 
  group_by(targetgroup) %&gt;% 
  slice_max(chi_abs, n = 20, with_ties = F) %&gt;% 
  filter(!is.nan(chi2))
  
# plot

ggplot(data = tstat_key_win_att, aes(x = reorder(feature, chi2), y = chi2, fill = dimension,
                                     label = feature)) + 
  geom_col(width = 0.7, linewidth = 0.2, color = &quot;black&quot;) +
  geom_text(color = &quot;gray25&quot;, hjust = &quot;outward&quot;, 
            position = position_nudge_center(y = 0.4,
                                               direction = &quot;split&quot;)
    ) +
  coord_flip() +
  scale_x_discrete(labels = translations_winatt) +
  scale_y_continuous(
    limits = c(-23, 30)
    ) +
  scale_fill_manual(values = c(&quot;gray1&quot;, 
                                      #&quot;gray40&quot;, 
                                      &quot;gray72&quot;),
                    na.value = &quot;gray99&quot;,
                    name = NULL, na.translate = F) +
  labs(x = NULL, y = &quot;Chi2&quot;) +
  theme(legend.position = &quot;bottom&quot;,
        axis.text.y = element_text(size = 8)) +
  annotate(geom = &quot;text&quot;, x = c(-1, 42), y = c(-10, 15), size = 4, color = &quot;black&quot;,
           label = c(&quot;social conservatives&quot;, &quot;progressives&quot;)) +
  expand_limits(x = c(-2, 43))</code></pre>
<p><img role="img" src="data:image/png;base64,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" width="960" /></p>
</div>
<div id="figure-5" class="section level1">
<h1>Figure 5</h1>
<pre class="r"><code>#translations
translations_loseatt &lt;- c(&quot;Germans&quot;, &quot;pensioners&quot;, &quot;country&quot;, &quot;foreigners&quot;, &quot;mid tier&quot;, &quot;the honest&quot;, &quot;don&#39;t know&quot;, &quot;the poor&quot;, &quot;citizens&quot;, &quot;to try&quot;, &quot;employees&quot;, &quot;to speak out&quot;, &quot;Germans&quot;, &quot;taken&quot;, &quot;no&quot;, &quot;low-income earners&quot;, &quot;important&quot;, &quot;heavy&quot;, &quot;the normal&quot;, &quot;serious&quot;, &quot;COVID skeptics&quot;, &quot;to reach&quot;, &quot;active&quot;, &quot;conservative&quot;, &quot;illness&quot;, &quot;performance&quot;, &quot;lawyers&quot;, &quot;big&quot;, &quot;nazis&quot;, &quot;social occupations&quot;, &quot;schooling&quot;,&quot;crafts&quot;, &quot;migration background&quot;, &quot;situation&quot;, &quot;academics&quot;, &quot;social occupations&quot;, &quot;teachers&quot;, &quot;opinions&quot;, &quot;bankers&quot;, &quot;gender inclusive form&quot;)

# compute keyness and merge to dictionary to categorize terms
tstat_key_lose_att &lt;- textstat_keyness(dfm_group(dfmat_lose, groups = dfmat_lose$galtan_group),
                                  target = &quot;progressive&quot;) %&gt;% 
  left_join(dict_df, by = c(&quot;feature&quot;)) %&gt;% 
  mutate(
    targetgroup = if_else(chi2 &gt;= 0, &quot;target&quot;, &quot;reference&quot;),
    chi_abs = abs(chi2) 
  ) %&gt;% 
  group_by(targetgroup) %&gt;% 
  slice_max(chi_abs, n = 20, with_ties = F) %&gt;% 
  filter(!is.nan(chi2))
  
# plot

ggplot(data = tstat_key_lose_att, aes(x = reorder(feature, chi2), y = chi2, fill = dimension,
                                     label = feature)) + 
  geom_col(width = 0.7, linewidth = 0.2, color = &quot;black&quot;) +
  geom_text(color = &quot;gray25&quot;, hjust = &quot;outward&quot;,
            position = position_nudge_center(y = 0.4,
                                               direction = &quot;split&quot;)
    ) +
  coord_flip() +
  scale_x_discrete(labels = translations_loseatt) +
  scale_y_continuous(
    limits = c(-28, 33)
    ) +
  scale_fill_manual(values = c(&quot;gray1&quot;, 
                                      &quot;gray40&quot;, 
                                      &quot;gray72&quot;),
                    na.value = &quot;gray99&quot;,
                    name = NULL, na.translate = F) +
  labs(x = NULL, y = &quot;Chi2&quot;) +
  theme(legend.position = &quot;bottom&quot;,
        axis.text.y = element_text(size = 8)) +
  annotate(geom = &quot;text&quot;, x = c(-1, 42), y = c(-10, 7.5), size = 4, color = &quot;black&quot;,
           label = c(&quot;social conservatives&quot;, &quot;progressives&quot;)) +
  expand_limits(x = c(-2, 43))</code></pre>
<p><img role="img" 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" width="960" /></p>
</div>
<div id="figure-6" class="section level1">
<h1>Figure 6</h1>
<pre class="r"><code># merge dictionary results to dataframe with respondent vars

df_p_windict &lt;- left_join(df_win_dict, df_p, by = &quot;ResponseId&quot;) %&gt;%
  mutate(sociocultural = if_else(rowSums(select(., starts_with(&quot;sociocultural.&quot;))) &gt;= 1, 1, 0),
         socioeconomic = if_else(rowSums(select(., starts_with(&quot;socioeconomic.&quot;))) &gt;= 1, 1, 0),
         soccult_race = if_else(sociocultural.race &gt;= 1, 1, 0),
         soccult_gendersex = if_else(rowSums(select(., starts_with(c(&quot;sociocultural.gender&quot;, &quot;sociocultural.sexuality&quot;)))) &gt;= 1, 1, 0))

df_p_losedict &lt;- left_join(df_lose_dict, df_p, by = &quot;ResponseId&quot;) %&gt;%
  mutate(sociocultural = if_else(rowSums(select(., starts_with(&quot;sociocultural.&quot;))) &gt;= 1, 1, 0),
         socioeconomic = if_else(rowSums(select(., starts_with(&quot;socioeconomic.&quot;))) &gt;= 1, 1, 0),
         soccult_race = if_else(sociocultural.race &gt;= 1, 1, 0),
         soccult_gendersex = if_else(rowSums(select(., starts_with(c(&quot;sociocultural.gender&quot;, &quot;sociocultural.sexuality&quot;)))) &gt;= 1, 1, 0))

# Panel A

model_soccultw3 &lt;- glm(sociocultural ~ galtan_group + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)

model_soccultl3 &lt;- glm(sociocultural ~ galtan_group + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_racel3 &lt;- glm(soccult_race ~ galtan_group + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_racew3 &lt;- glm(soccult_race ~ galtan_group + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)

model_gsl3 &lt;- glm(soccult_gendersex ~ galtan_group + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_gsw3 &lt;- glm(soccult_gendersex ~ galtan_group + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)

# all sociocultural

pred_scl &lt;- avg_predictions(model_soccultl3, by = &quot;galtan_group&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as losing groups&quot;,
         outcome = &quot;overall\nsociocultural&quot;)
pred_scw &lt;- avg_predictions(model_soccultw3, by = &quot;galtan_group&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as winning groups&quot;,
         outcome = &quot;overall\nsociocultural&quot;)


# race
pred_scrl &lt;- avg_predictions(model_racel3, by = &quot;galtan_group&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as losing groups&quot;,
         outcome = &quot;race&quot;)
pred_scrw &lt;- avg_predictions(model_racew3, by = &quot;galtan_group&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as winning groups&quot;,
         outcome = &quot;race&quot;)

# gender

pred_scgl &lt;- avg_predictions(model_gsl3, by = &quot;galtan_group&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as losing groups&quot;,
         outcome = &quot;gender and\nsexuality&quot;)
pred_scgw &lt;- avg_predictions(model_gsw3, by = &quot;galtan_group&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as winning groups&quot;,
         outcome = &quot;gender and\nsexuality&quot;)

# combine

pred_cult &lt;- bind_rows(pred_scl, pred_scw, pred_scrl, pred_scrw, pred_scgl, pred_scgw) %&gt;% 
  mutate(outcome = fct_relevel(factor(outcome), 
                               &quot;overall\nsociocultural&quot;, &quot;race&quot;, &quot;gender and\nsexuality&quot;),
         question = fct_rev(factor(question)))

gal &lt;- ggplot(pred_cult, aes(outcome, estimate, color = galtan_group)) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high),
                  position = position_dodge(width = 0.5)) +
  facet_wrap(~question) +
  scale_color_grey(start = 0.1, end = 0.7, name = &quot;Subgroup&quot;)+
  theme(legend.position = &quot;bottom&quot;)+
  labs(y = &quot;Predicted probability of mentioning groups&quot;, x = NULL,
       subtitle = &quot;A) By sociocultural attitudes&quot;
       )

# Panel B

model_soccultl4 &lt;- glm(sociocultural ~ galtan_att + att_socialben + age + gender_edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_soccultw4 &lt;- glm(sociocultural ~ galtan_att + att_socialben + age + gender_edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)

model_racel4 &lt;- glm(soccult_race ~ galtan_att + att_socialben + age + gender_edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_racew4 &lt;- glm(soccult_race ~ galtan_att + att_socialben + age + gender_edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)

model_gsl4 &lt;- glm(soccult_gendersex ~ galtan_att + att_socialben + age + gender_edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_gsw4 &lt;- glm(soccult_gendersex ~ galtan_att + att_socialben + age + gender_edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)

pred_scl &lt;- avg_predictions(model_soccultl4, by = &quot;gender_edu&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as losing groups&quot;,
         outcome = &quot;overall\nsociocultural&quot;)
pred_scw &lt;- avg_predictions(model_soccultw4, by = &quot;gender_edu&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as winning groups&quot;,
         outcome = &quot;overall\nsociocultural&quot;)

# race
pred_scrl &lt;- avg_predictions(model_racel4, by = &quot;gender_edu&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as losing groups&quot;,
         outcome = &quot;race&quot;)
pred_scrw &lt;- avg_predictions(model_racew4, by = &quot;gender_edu&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as winning groups&quot;,
         outcome = &quot;race&quot;)

# gender

pred_scgl &lt;- avg_predictions(model_gsl4, by = &quot;gender_edu&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as losing groups&quot;,
         outcome = &quot;gender and\nsexuality&quot;)
pred_scgw &lt;- avg_predictions(model_gsw4, by = &quot;gender_edu&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as winning groups&quot;,
         outcome = &quot;gender and\nsexuality&quot;)

# combine

pred_cult &lt;- bind_rows(pred_scl, pred_scw, pred_scrl, pred_scrw, pred_scgl, pred_scgw) %&gt;% 
  mutate(outcome = fct_relevel(factor(outcome), 
                               &quot;overall\nsociocultural&quot;, &quot;race&quot;, &quot;gender and\nsexuality&quot;),
         question = fct_rev(factor(question)))

ge &lt;- ggplot(pred_cult, aes(outcome, estimate, color = gender_edu)) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high),
                  position = position_dodge(width = 0.5)) +
  facet_wrap(~question) +
  scale_color_grey(start = 0, end = 0.8, name = NULL)+
  theme(legend.position = &quot;bottom&quot;)+
  labs(y = &quot;Predicted probability of mentioning groups&quot;, x = NULL,
       subtitle = &quot;B) By gender and education&quot;
       )

fig6 &lt;- arrangeGrob(gal, ge,
                    nrow = 2, heights = unit(c(12, 12), c(&quot;cm&quot;)))

grid.arrange(fig6)</code></pre>
<p><img role="img" 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" width="768" /></p>
</div>
<div id="appendix" class="section level1">
<h1>Appendix</h1>
<div id="table-a1" class="section level2">
<h2>Table A1</h2>
<pre class="r"><code># nicer labels
 df1 &lt;- df_p %&gt;% 
  mutate(
    party_block_nice = fct_collapse(party_vote,
                               &quot;left party&quot; = c(&quot;Sozialdemokratische Partei Deutschlands (SPD)&quot;,
                                              &quot;Bündnis 90/Die Grünen&quot;,
                                              &quot;Die Linke&quot;),
                               &quot;right party&quot; = c(&quot;Christlich Demokratische Union/Christlich-Soziale Union (CDU/CSU)&quot;,
                                               &quot;Freie Demokratische Partei (FDP)&quot;,
                                               &quot;Alternative für Deutschland (AfD)&quot;),
                               &quot;other or none&quot; = c(&quot;anderer Partei&quot;, &quot;keiner Partei&quot;)),
    party_block_nice = fct_relevel(party_block_nice, &quot;right party&quot;, &quot;left party&quot;, &quot;other or none&quot;),
    party_vote_nice = fct_recode(party_vote,
                                 &quot;Social democrats (SPD)&quot; = &quot;Sozialdemokratische Partei Deutschlands (SPD)&quot;,
                                 &quot;Green party&quot; = &quot;Bündnis 90/Die Grünen&quot;,
                                 &quot;Left party (LINKE)&quot; = &quot;Die Linke&quot;,
                                 &quot;Christian democrats (CDU/CSU)&quot; = &quot;Christlich Demokratische Union/Christlich-Soziale Union (CDU/CSU)&quot;,
                                 &quot;Liberal party (FDP)&quot; = &quot;Freie Demokratische Partei (FDP)&quot;,
                                 &quot;Alternative for Germany (AfD)&quot; = &quot;Alternative für Deutschland (AfD)&quot;,
                                 &quot;other party&quot; = &quot;anderer Partei&quot;, 
                                 NULL = &quot;keiner Partei&quot;),
    party_vote_nice = fct_relevel(party_vote_nice,&quot;Social democrats (SPD)&quot;, &quot;Christian democrats (CDU/CSU)&quot;,
                                  &quot;Green party&quot;, &quot;Liberal party (FDP)&quot;, &quot;Alternative for Germany (AfD)&quot;,
                                  &quot;Left party (LINKE)&quot;, &quot;other party&quot;),
    gender_nice = fct_recode(gender, woman = &quot;female&quot;,
                        man = &quot;male&quot;,
                        nonbinary = &quot;nonbinary&quot;),
    gender_nice = fct_relevel(gender_nice, &quot;woman&quot;, &quot;man&quot;, &quot;nonbinary&quot;)
  )


sample &lt;- df1 %&gt;%
  select(`Gender` = gender_nice, 
         `Income` = income_group_eq, 
         `Education` = edu, 
         `Age` = age_group, 
         `Migration background` = migrationb, 
         `Residence` = urban,
         `Vote choice` = party_vote_nice)


datasummary(data = sample, 
                 `Gender` + `Education` + `Income` + `Age` + `Migration background` + `Residence` + `Vote choice` + 
                   1 ~  N + Percent(), 
                 notes = c(
                   &quot;Vote choice: party voted in 2021 (or would have voted, if non-citizen);
                   Residence: selfreported degree of urbanity/rurality;
                   Migration background: respondent or at least one parent born abroad;
                   Income: equivalized according to household size.&quot;
                           ),
            title = &quot;Sample characteristics&quot;
                 #output = &quot;latex&quot;
                 )</code></pre>
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    <div class="container">
      <table class="table table-borderless" id="tinytable_jw5a7hqmxnm0jizygv4q" style="width: auto; margin-left: auto; margin-right: auto;" data-quarto-disable-processing="true">
        <thead>
        <caption>Sample characteristics</caption>
              <tr>
                <th scope="col"> </th>
                <th scope="col">  </th>
                <th scope="col">N</th>
                <th scope="col">Percent</th>
              </tr>
        </thead>
        <tfoot><tr><td colspan="4">Vote choice: party voted in 2021 (or would have voted, if non-citizen);
                   Residence: selfreported degree of urbanity/rurality;
                   Migration background: respondent or at least one parent born abroad;
                   Income: equivalized according to household size.</td></tr></tfoot>
        <tbody>
                <tr>
                  <td>Gender</td>
                  <td>woman</td>
                  <td>1934</td>
                  <td>50.81</td>
                </tr>
                <tr>
                  <td></td>
                  <td>man</td>
                  <td>1863</td>
                  <td>48.95</td>
                </tr>
                <tr>
                  <td></td>
                  <td>nonbinary</td>
                  <td>9</td>
                  <td>0.24</td>
                </tr>
                <tr>
                  <td>Education</td>
                  <td>low education</td>
                  <td>776</td>
                  <td>20.39</td>
                </tr>
                <tr>
                  <td></td>
                  <td>medium education</td>
                  <td>2028</td>
                  <td>53.28</td>
                </tr>
                <tr>
                  <td></td>
                  <td>high education</td>
                  <td>1002</td>
                  <td>26.33</td>
                </tr>
                <tr>
                  <td>Income</td>
                  <td>low income</td>
                  <td>1501</td>
                  <td>39.44</td>
                </tr>
                <tr>
                  <td></td>
                  <td>medium income</td>
                  <td>1115</td>
                  <td>29.30</td>
                </tr>
                <tr>
                  <td></td>
                  <td>high income</td>
                  <td>874</td>
                  <td>22.96</td>
                </tr>
                <tr>
                  <td>Age</td>
                  <td>18-39 years</td>
                  <td>1400</td>
                  <td>36.78</td>
                </tr>
                <tr>
                  <td></td>
                  <td>40-59 years</td>
                  <td>1451</td>
                  <td>38.12</td>
                </tr>
                <tr>
                  <td></td>
                  <td>above 60 years</td>
                  <td>894</td>
                  <td>23.49</td>
                </tr>
                <tr>
                  <td>Migration background</td>
                  <td>no</td>
                  <td>3259</td>
                  <td>85.63</td>
                </tr>
                <tr>
                  <td></td>
                  <td>yes</td>
                  <td>542</td>
                  <td>14.24</td>
                </tr>
                <tr>
                  <td>Residence</td>
                  <td>countryside</td>
                  <td>888</td>
                  <td>23.33</td>
                </tr>
                <tr>
                  <td></td>
                  <td>suburb/small city</td>
                  <td>1897</td>
                  <td>49.84</td>
                </tr>
                <tr>
                  <td></td>
                  <td>big city</td>
                  <td>1017</td>
                  <td>26.72</td>
                </tr>
                <tr>
                  <td>Vote choice</td>
                  <td>Social democrats (SPD)</td>
                  <td>863</td>
                  <td>22.67</td>
                </tr>
                <tr>
                  <td></td>
                  <td>Christian democrats (CDU/CSU)</td>
                  <td>546</td>
                  <td>14.35</td>
                </tr>
                <tr>
                  <td></td>
                  <td>Green party</td>
                  <td>601</td>
                  <td>15.79</td>
                </tr>
                <tr>
                  <td></td>
                  <td>Liberal party (FDP)</td>
                  <td>427</td>
                  <td>11.22</td>
                </tr>
                <tr>
                  <td></td>
                  <td>Alternative for Germany (AfD)</td>
                  <td>347</td>
                  <td>9.12</td>
                </tr>
                <tr>
                  <td></td>
                  <td>Left party (LINKE)</td>
                  <td>246</td>
                  <td>6.46</td>
                </tr>
                <tr>
                  <td></td>
                  <td>other party</td>
                  <td>308</td>
                  <td>8.09</td>
                </tr>
                <tr>
                  <td></td>
                  <td>All</td>
                  <td>3806</td>
                  <td>100.00</td>
                </tr>
        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
<div id="figure-a1" class="section level2">
<h2>Figure A1</h2>
<div id="wave-1-winning-groups" class="section level4">
<h4>Wave 1 Winning groups</h4>
<pre class="r"><code># 1. remove short answers

df_p &lt;- df_p %&gt;% 
  mutate(nchar_win = nchar(rg_winning)) 

df_p_keepwinw1 &lt;- df_p %&gt;% 
  filter(wave == &quot;w1&quot;) %&gt;% 
  filter(nchar_win &gt;=3)

# 2. corpus, tokenization, lower case

corp_rgwinw1 &lt;- corpus(df_p_keepwinw1, text_field = &quot;rg_winning&quot;)

toks_winw1 &lt;- tokens(corp_rgwinw1, remove_punct = TRUE, remove_symbols = TRUE, padding = FALSE) %&gt;% 
  tokens_tolower()

# 3. compound those expressions that have meaning (see above)

toks_win_compw1 &lt;- tokens_compound(toks_winw1, pattern = phrase(comp_winning)) 

# 4. stopwords and other terms to delete

toks_win_compw1 &lt;- toks_win_compw1 %&gt;% 
  tokens_remove(pattern = c(stopwords(&quot;de&quot;, source = 
                        &quot;marimo&quot;), delete), 
                valuetype = &quot;fixed&quot;, padding = TRUE)

# 5. text matrix and delete white space

dfmat_winw1 &lt;- toks_win_compw1 %&gt;% 
  dfm() %&gt;% 
  dfm_trim(max_termfreq = 5000, termfreq_type = &quot;count&quot;) # to remove white space (or tab)</code></pre>
</div>
<div id="wave-1-losing-groups" class="section level4">
<h4>Wave 1 Losing groups</h4>
<pre class="r"><code># 1. remove short answers

df_p &lt;- df_p %&gt;% 
  mutate(nchar_lose = nchar(rg_losing)) 

df_p_keeplosew1 &lt;- df_p %&gt;% 
  filter(wave == &quot;w1&quot;) %&gt;% 
  filter(nchar_lose &gt;=3)

# 2. corpus, tokenization, lower case

corp_rglosew1 &lt;- corpus(df_p_keeplosew1, text_field = &quot;rg_losing&quot;)

# tokens

toks_losew1 &lt;- tokens(corp_rglosew1, remove_punct = TRUE, remove_symbols = T, padding = F) %&gt;% 
  tokens_tolower()

# 3. compound those expressions that have meaning

toks_lose_compw1 &lt;- tokens_compound(toks_losew1, pattern = phrase(comp_losing))

# 4. stopwords and other terms to delete

toks_lose_compw1 &lt;- toks_lose_compw1 %&gt;% 
  tokens_remove(pattern = c(stopwords(&quot;de&quot;, source = 
                        &quot;marimo&quot;), 
                        delete
                        ), 
                valuetype = &quot;fixed&quot;, padding = TRUE)

# 5. text matrix and delete white space

dfmat_losew1 &lt;- toks_lose_compw1 %&gt;% 
  dfm() %&gt;% 
  dfm_trim(max_termfreq = 5000, termfreq_type = &quot;count&quot;) # to remove white space (or tab)</code></pre>
</div>
<div id="wave-1-high-groups" class="section level4">
<h4>Wave 1 High groups</h4>
<pre class="r"><code># 1. remove short answers

df_p &lt;- df_p %&gt;% 
  mutate(nchar_high = nchar(rg_high)) 

df_p_keephighw1 &lt;- df_p %&gt;% 
  filter(wave == &quot;w1&quot;) %&gt;% 
  filter(nchar_high &gt;=3)

# 2. corpus, tokenization, lower case

corp_rghighw1 &lt;- corpus(df_p_keephighw1, text_field = &quot;rg_high&quot;)

# tokens

toks_highw1 &lt;- tokens(corp_rghighw1, remove_punct = TRUE, remove_symbols = T, padding = F) %&gt;% 
  tokens_tolower()

# 3. compound those expressions that have meaning:

toks_high_compw1 &lt;- tokens_compound(toks_highw1, pattern = phrase(comp_high))

# 4. stopwords and other terms to delete

toks_high_compw1 &lt;- toks_high_compw1 %&gt;% 
  tokens_remove(pattern = c(stopwords(&quot;de&quot;, source = 
                        &quot;marimo&quot;), delete, &quot;rand&quot;, &quot;oben&quot;, &quot;oberen&quot;), 
                valuetype = &quot;fixed&quot;, padding = TRUE)

# 5. text matrix and delete white space

dfmat_highw1 &lt;- toks_high_compw1 %&gt;% 
  dfm() %&gt;% 
  dfm_trim(max_termfreq = 5000, termfreq_type = &quot;count&quot;) # to remove white space (or tab)</code></pre>
</div>
<div id="wave-1-low-groups" class="section level4">
<h4>Wave 1 Low groups</h4>
<pre class="r"><code># 1. remove short answers

df_p &lt;- df_p %&gt;% 
  mutate(nchar_low = nchar(rg_low)) 

df_p_keeploww1 &lt;- df_p %&gt;% 
  filter(wave == &quot;w1&quot;) %&gt;% 
  filter(nchar_low &gt;=3)

# 2. corpus, tokenization, lower case

corp_rgloww1 &lt;- corpus(df_p_keeploww1, text_field = &quot;rg_low&quot;)

# tokens

toks_loww1 &lt;- tokens(corp_rgloww1, remove_punct = TRUE, remove_symbols = TRUE, padding = F) %&gt;% 
  tokens_tolower()

# 3. compound

toks_low_compw1 &lt;- tokens_compound(toks_loww1, pattern = phrase(comp_low))

# 4. stopwords and other terms to delete

toks_low_compw1 &lt;- toks_low_compw1 %&gt;% 
  tokens_remove(pattern = c(stopwords(&quot;de&quot;, source = 
                        &quot;marimo&quot;), delete, &quot;rand&quot;, &quot;unteren&quot;, &quot;oberen&quot;), 
                valuetype = &quot;fixed&quot;, padding = TRUE)

# 5. text matrix and delete white space

dfmat_loww1 &lt;- toks_low_compw1 %&gt;% 
  dfm() %&gt;% 
  dfm_trim(max_termfreq = 5000, termfreq_type = &quot;count&quot;) # to remove white space (or tab)</code></pre>
<pre class="r"><code># HIGH

high_dimw1 &lt;- textstat_frequency(dfmat_highw1, n = 20)

translations_high &lt;- c(&quot;to earn&quot;, &quot;the wealthy&quot;, &quot;to work&quot;, &quot;millionaires&quot;, &quot;egoistic&quot;, &quot;the rich&quot;, &quot;wealthy&quot;, &quot;heirs&quot;, &quot;wealth&quot;, &quot;academics&quot;, &quot;arrogant&quot;, &quot;power&quot;, &quot;doctors&quot;, &quot;entrepreneurs&quot;, &quot;a lot of money&quot;, &quot;managers&quot;, &quot;rich&quot;, &quot;money&quot;, &quot;the rich&quot;, &quot;politicians&quot;)

high_dimw1 &lt;- dict_df %&gt;% 
  left_join(high_dimw1, ., by = c(&quot;feature&quot;))

plot_high_fw1 &lt;- high_dimw1 %&gt;%  
  ggplot(aes(x = reorder(feature, frequency), y = frequency, fill = dimension)) +
  geom_col(width = 0.7, size = 0.2, color = &quot;black&quot;) +
  coord_flip() +
  scale_y_continuous(limits = c(0, 410)) +
  scale_fill_manual(values = c(&quot;gray1&quot;, &quot;gray40&quot;, &quot;gray72&quot;),
                    na.value = &quot;gray99&quot;,
                    name = NULL,
                    na.translate = F) +
    annotate(geom = &quot;text&quot;, x = 1:20, y = 410, size = 2.5, color = &quot;gray25&quot;, 
             label = translations_high, hjust = 1)+
  labs(x = NULL, y = &quot;Frequency&quot;, subtitle = &quot;Wave 1 - Static: high&quot;)+
  theme(legend.position = &quot;bottom&quot;,
        axis.text.y = element_text(size = 8))

# LOW

low_dimw1 &lt;- textstat_frequency(dfmat_loww1, n = 20)

translations_low &lt;- c(&quot;work&quot;, &quot;migration background&quot;,  &quot;state&quot;, &quot;poor&quot;, &quot;money&quot;, &quot;welfare recipients&quot;, &quot;low-income earners&quot;, &quot;income&quot;, &quot;to work&quot;, &quot;education&quot;, &quot;hartz 4&quot;, &quot;the sick&quot;, &quot;single parents&quot;, &quot;recipients&quot;, &quot;the poor&quot;, &quot;migrants&quot;, &quot;homeless&quot;, &quot;workers&quot;, &quot;pensioners&quot;, &quot;the unemployed&quot;)

low_dimw1 &lt;- dict_df %&gt;% 
  left_join(low_dimw1, ., by = c(&quot;feature&quot;))

plot_low_fw1 &lt;- low_dimw1 %&gt;%  
  ggplot(aes(x = reorder(feature, frequency), y = frequency, fill = dimension)) +
  geom_col(width = 0.7,  size = 0.2, color = &quot;black&quot;) +
  coord_flip() +
  scale_fill_manual(values = c(&quot;gray1&quot;, &quot;gray40&quot;, &quot;gray72&quot;),
                    na.value = &quot;gray99&quot;,
                    name = NULL,
                    na.translate = F) +
    annotate(geom = &quot;text&quot;, x = 1:20, y = 460, size = 2.5, color = &quot;gray25&quot;, 
             label = translations_low, hjust = 1)+
  labs(x = NULL, y = &quot;Frequency&quot;, subtitle = &quot;Wave 1 - Static: low&quot;)+
  theme(legend.position = &quot;bottom&quot;,
         axis.text.y = element_text(size = 8))</code></pre>
<pre class="r"><code># Winning

win_dimw1 &lt;-  textstat_frequency(dfmat_winw1, n = 20)


translations_win &lt;- c(&quot;women&quot;, &quot;craftsmen&quot;, &quot;money&quot;, &quot;the rich&quot;, &quot;to work&quot;, &quot;influencers&quot;,&quot;no idea&quot;, &quot;doctors&quot;,  &quot;academics&quot;, &quot;politicians&quot;, &quot;graduates&quot;, &quot;homosexuals&quot;, &quot;time&quot;, &quot;occupations&quot;, &quot;work&quot;, &quot;children&quot;, &quot;migrants&quot;, &quot;a lot of money&quot;, &quot;social&quot;, &quot;workers&quot;)


win_dimw1 &lt;- dict_df %&gt;% 
  left_join(win_dimw1, ., by = c(&quot;feature&quot;))

plot_win_fw1 &lt;- win_dimw1 %&gt;% 
  ggplot(aes(x = reorder(feature, frequency), y = frequency, fill = dimension)) +
  geom_col(width = 0.7, size = 0.2, color = &quot;black&quot;) +
  coord_flip() +
  scale_y_continuous(limits = c(0, 290)) +
  scale_fill_manual(values = c(&quot;gray1&quot;, &quot;gray40&quot;, &quot;gray72&quot;),
                    na.value = &quot;gray99&quot;,
                    name = NULL,
                    na.translate = F) +
  annotate(geom = &quot;text&quot;, x = 1:20, y = 290, size = 2.5, color = &quot;gray25&quot;, 
           label = rev(translations_win), hjust = 1)+
  labs(x = NULL, y = &quot;Frequency&quot;, subtitle = &quot;Wave 1 - Dynamic: winning&quot;) +
  theme(legend.position = &quot;bottom&quot;,
         axis.text.y = element_text(size = 8))

# Losing

lose_dimw1 &lt;-  textstat_frequency(dfmat_losew1, n = 20)

translations_lose &lt;- c(&quot;foreigners&quot;, &quot;doctors&quot;, &quot;education&quot;, &quot;migrants&quot;, &quot;the poor&quot;, &quot;money&quot;, &quot;don&#39;t know&quot;, &quot;occupations&quot;, &quot;civil servants&quot;, &quot;the unemployed&quot;, &quot;the elderly&quot;,  &quot;pensioners&quot;, &quot;policemen&quot;,  &quot;work&quot;, &quot;workers&quot;, &quot;to work&quot;, &quot;teachers&quot;, &quot;police&quot;, &quot;craftsmen&quot;, &quot;politicians&quot;)

lose_dimw1 &lt;- dict_df %&gt;% 
  left_join(lose_dimw1, ., by = c(&quot;feature&quot;))

plot_lose_fw1 &lt;- lose_dimw1 %&gt;% 
  ggplot(aes(x = reorder(feature, frequency), y = frequency, fill = dimension)) +
  geom_col(width = 0.7, size = 0.2, color = &quot;black&quot;) +
  coord_flip() +
  scale_fill_manual(values = c(&quot;gray1&quot;, &quot;gray40&quot;, 
                               &quot;gray72&quot;),
                    na.value = &quot;gray99&quot;,
                    name = NULL,
                    na.translate = F) +
  annotate(geom = &quot;text&quot;, x = 1:20, y = 400, size = 2.5, color = &quot;gray25&quot;, 
           label = translations_lose, hjust = 1)+
  labs(x = NULL, y = &quot;Frequency&quot;, subtitle = &quot;Wave 1 - Dynamic: losing&quot;) +
  theme(legend.position = &quot;bottom&quot;,
         axis.text.y = element_text(size = 8))</code></pre>
</div>
<div id="wave-2-winning-groups" class="section level4">
<h4>Wave 2 Winning groups</h4>
<pre class="r"><code># 1. remove short answers

df_p &lt;- df_p %&gt;% 
  mutate(nchar_win = nchar(rg_winning)) 

df_p_keepwinw2 &lt;- df_p %&gt;% 
  filter(wave == &quot;w2&quot;) %&gt;% 
  filter(nchar_win &gt;=3)

# 2. corpus, tokenization, lower case

corp_rgwinw2 &lt;- corpus(df_p_keepwinw2, text_field = &quot;rg_winning&quot;)

toks_winw2 &lt;- tokens(corp_rgwinw2, remove_punct = TRUE, remove_symbols = TRUE, padding = FALSE) %&gt;% 
  tokens_tolower()

# 3. compound those expressions that have meaning (see above)

toks_win_compw2 &lt;- tokens_compound(toks_winw2, pattern = phrase(comp_winning)) 

# 4. stopwords and other terms to delete

toks_win_compw2 &lt;- toks_win_compw2 %&gt;% 
  tokens_remove(pattern = c(stopwords(&quot;de&quot;, source = 
                        &quot;marimo&quot;), delete), 
                valuetype = &quot;fixed&quot;, padding = TRUE)

# 5. text matrix and delete white space

dfmat_winw2 &lt;- toks_win_compw2 %&gt;% 
  dfm() %&gt;% 
  dfm_trim(max_termfreq = 5000, termfreq_type = &quot;count&quot;) # to remove white space (or tab)</code></pre>
</div>
<div id="wave-2-losing-groups" class="section level4">
<h4>Wave 2 Losing groups</h4>
<pre class="r"><code># 1. remove short answers

df_p &lt;- df_p %&gt;% 
  mutate(nchar_lose = nchar(rg_losing)) 

df_p_keeplosew2 &lt;- df_p %&gt;% 
  filter(wave == &quot;w2&quot;) %&gt;% 
  filter(nchar_lose &gt;=3)

# 2. corpus, tokenization, lower case

corp_rglosew2 &lt;- corpus(df_p_keeplosew2, text_field = &quot;rg_losing&quot;)

# tokens

toks_losew2 &lt;- tokens(corp_rglosew2, remove_punct = TRUE, remove_symbols = T, padding = F) %&gt;% 
  tokens_tolower()

# 3. compound those expressions that have meaning

toks_lose_compw2 &lt;- tokens_compound(toks_losew2, pattern = phrase(comp_losing))

# 4. stopwords and other terms to delete

toks_lose_compw2 &lt;- toks_lose_compw2 %&gt;% 
  tokens_remove(pattern = c(stopwords(&quot;de&quot;, source = 
                        &quot;marimo&quot;), 
                        delete
                        ), 
                valuetype = &quot;fixed&quot;, padding = TRUE)

# 5. text matrix and delete white space

dfmat_losew2 &lt;- toks_lose_compw2 %&gt;% 
  dfm() %&gt;% 
  dfm_trim(max_termfreq = 5000, termfreq_type = &quot;count&quot;) # to remove white space (or tab)</code></pre>
</div>
<div id="wave-2-high-groups" class="section level4">
<h4>Wave 2 High groups</h4>
<pre class="r"><code># 1. remove short answers

df_p &lt;- df_p %&gt;% 
  mutate(nchar_high = nchar(rg_high)) 

df_p_keephighw2 &lt;- df_p %&gt;% 
  filter(wave == &quot;w2&quot;) %&gt;% 
  filter(nchar_high &gt;=3)

# 2. corpus, tokenization, lower case

corp_rghighw2 &lt;- corpus(df_p_keephighw2, text_field = &quot;rg_high&quot;)

# tokens

toks_highw2 &lt;- tokens(corp_rghighw2, remove_punct = TRUE, remove_symbols = T, padding = F) %&gt;% 
  tokens_tolower()

# 3. compound those expressions that have meaning:

toks_high_compw2 &lt;- tokens_compound(toks_highw2, pattern = phrase(comp_high))

# 4. stopwords and other terms to delete

toks_high_compw2 &lt;- toks_high_compw2 %&gt;% 
  tokens_remove(pattern = c(stopwords(&quot;de&quot;, source = 
                        &quot;marimo&quot;), delete, &quot;rand&quot;, &quot;oben&quot;, &quot;oberen&quot;), 
                valuetype = &quot;fixed&quot;, padding = TRUE)

# 5. text matrix and delete white space

dfmat_highw2 &lt;- toks_high_compw2 %&gt;% 
  dfm() %&gt;% 
  dfm_trim(max_termfreq = 5000, termfreq_type = &quot;count&quot;) # to remove white space (or tab)</code></pre>
</div>
<div id="wave-2-low-groups" class="section level4">
<h4>Wave 2 Low groups</h4>
<pre class="r"><code># 1. remove short answers

df_p &lt;- df_p %&gt;% 
  mutate(nchar_low = nchar(rg_low)) 

df_p_keeploww2 &lt;- df_p %&gt;% 
  filter(wave == &quot;w2&quot;) %&gt;% 
  filter(nchar_low &gt;=3)

# 2. corpus, tokenization, lower case

corp_rgloww2 &lt;- corpus(df_p_keeploww2, text_field = &quot;rg_low&quot;)

# tokens

toks_loww2 &lt;- tokens(corp_rgloww2, remove_punct = TRUE, remove_symbols = TRUE, padding = F) %&gt;% 
  tokens_tolower()

# 3. compound

toks_low_compw2 &lt;- tokens_compound(toks_loww2, pattern = phrase(comp_low))

# 4. stopwords and other terms to delete

toks_low_compw2 &lt;- toks_low_compw2 %&gt;% 
  tokens_remove(pattern = c(stopwords(&quot;de&quot;, source = 
                        &quot;marimo&quot;), delete, &quot;rand&quot;, &quot;unteren&quot;, &quot;oberen&quot;), 
                valuetype = &quot;fixed&quot;, padding = TRUE)

# 5. text matrix and delete white space

dfmat_loww2 &lt;- toks_low_compw2 %&gt;% 
  dfm() %&gt;% 
  dfm_trim(max_termfreq = 5000, termfreq_type = &quot;count&quot;) # to remove white space (or tab)</code></pre>
<pre class="r"><code># HIGH

high_dim2 &lt;- textstat_frequency(dfmat_highw2, n = 20)

translations_high &lt;- c(&quot;academics&quot;, &quot;managers&quot;, &quot;education&quot;, &quot;don&#39;t know&quot;, &quot;influence&quot;, &quot;no idea&quot;, &quot;entrepreneurs&quot;, &quot;wealthy&quot;, &quot;wealth&quot;, &quot;to work&quot;, &quot;egoistic&quot;, &quot;aloof&quot;, &quot;arrogant&quot;, &quot;doctors&quot;, &quot;rich&quot;, &quot;power&quot;, &quot;the rich&quot;, &quot;a lot of money&quot;, &quot;politicians&quot;, &quot;money&quot;)

high_dim2 &lt;- dict_df %&gt;% 
  left_join(high_dim2, ., by = c(&quot;feature&quot;))

plot_high_fw2 &lt;- high_dim2 %&gt;%  
  ggplot(aes(x = reorder(feature, frequency), y = frequency, fill = dimension)) +
  geom_col(width = 0.7, size = 0.2, color = &quot;black&quot;) +
  coord_flip() +
  scale_y_continuous(limits = c(0, 410)) +
  scale_fill_manual(values = c(&quot;gray1&quot;, &quot;gray40&quot;, &quot;gray72&quot;
                               ),
                    na.value = &quot;gray99&quot;,
                    name = NULL,
                    na.translate = F) +
    annotate(geom = &quot;text&quot;, x = 1:20, y = 410, size = 2.5, color = &quot;gray25&quot;, 
             label = translations_high, hjust = 1)+
  labs(x = NULL, y = &quot;Frequency&quot;, subtitle = &quot;Wave 2 - Static: high&quot;)+
  theme(legend.position = &quot;bottom&quot;,
        axis.text.y = element_text(size = 8))

# LOW

low_dim2 &lt;- textstat_frequency(dfmat_loww2, n = 20)

translations_low &lt;- c(&quot;unemployed&quot;, &quot;to afford&quot;, &quot;migration background&quot;, &quot;craftsmen&quot;, &quot;single parents&quot;, &quot;hartz 4&quot;, &quot;work&quot;, &quot;lazy&quot;,  &quot;state&quot;, &quot;welfare recipients&quot;, &quot;workers&quot;,  &quot;poor&quot;,&quot;to work&quot;, &quot;money&quot;, &quot;the poor&quot;, &quot;recipients&quot;, &quot;migrants&quot;, &quot;homeless&quot;, &quot;pensioners&quot;, &quot;the unemployed&quot;)

low_dim2 &lt;- dict_df %&gt;% 
  left_join(low_dim2, ., by = c(&quot;feature&quot;))

plot_low_fw2 &lt;- low_dim2 %&gt;%  
  ggplot(aes(x = reorder(feature, frequency), y = frequency, fill = dimension)) +
  geom_col(width = 0.7,  size = 0.2, color = &quot;black&quot;) +
  coord_flip() +
  scale_fill_manual(values = c(&quot;gray1&quot;, #&quot;gray40&quot;, 
                               &quot;gray72&quot;),
                    na.value = &quot;gray99&quot;,
                    name = NULL,
                    na.translate = F) +
    annotate(geom = &quot;text&quot;, x = 1:20, y = 460, size = 2.5, color = &quot;gray25&quot;, 
             label = translations_low, hjust = 1)+
  labs(x = NULL, y = &quot;Frequency&quot;, subtitle = &quot;Wave 2 - Static: low&quot;)+
  theme(legend.position = &quot;bottom&quot;,
         axis.text.y = element_text(size = 8))</code></pre>
<pre class="r"><code># Winning

win_dimw2 &lt;-  textstat_frequency(dfmat_winw2, n = 20)


translations_win &lt;- c(&quot;women&quot;, &quot;craftsmen&quot;, &quot;money&quot;, &quot;influencers&quot;, &quot;the rich&quot;, &quot;to work&quot;, &quot;doctors&quot;, &quot;homosexuals&quot;, &quot;don&#39;t know&quot;, &quot;migrants&quot;, &quot;no idea&quot;, &quot;work&quot;,&quot;academics&quot;, &quot;to earn&quot;, &quot;politicians&quot;, &quot;to afford&quot;, &quot;education&quot;, &quot;foreigners&quot;, &quot;children&quot;, &quot;family&quot;)

win_dimw2 &lt;- dict_df %&gt;% 
  left_join(win_dimw2, ., by = c(&quot;feature&quot;))

plot_win_fw2 &lt;- win_dimw2 %&gt;% 
  ggplot(aes(x = reorder(feature, frequency), y = frequency, fill = dimension)) +
  geom_col(width = 0.7, size = 0.2, color = &quot;black&quot;) +
  coord_flip() +
  scale_y_continuous(limits = c(0, 290)) +
  scale_fill_manual(values = c(&quot;gray1&quot;, &quot;gray40&quot;, &quot;gray72&quot;),
                    na.value = &quot;gray99&quot;,
                    name = NULL,
                    na.translate = F) +
  annotate(geom = &quot;text&quot;, x = 1:20, y = 290, size = 2.5, color = &quot;gray25&quot;, 
           label = rev(translations_win), hjust = 1)+
  labs(x = NULL, y = &quot;Frequency&quot;, subtitle = &quot;Wave 2 - Dynamic: winning&quot;) +
  theme(legend.position = &quot;bottom&quot;,
         axis.text.y = element_text(size = 8))

# Losing

lose_dimw2 &lt;-  textstat_frequency(dfmat_losew2, n = 20)

translations_lose &lt;- c(&quot;education&quot;, &quot;occupations&quot;, &quot;the elderly&quot;, &quot;crafts&quot;, &quot;no idea&quot;, &quot;men&quot;, &quot;middle stratum&quot;, &quot;doctors&quot;, &quot;the unemployed&quot;, &quot;don&#39;t know&quot;, &quot;pensioners&quot;, &quot;mid tier&quot;,  &quot;policemen&quot;, &quot;workers&quot;, &quot;work&quot;, &quot;police&quot;, &quot;to work&quot;, &quot;teachers&quot;, &quot;craftsmen&quot;, &quot;politicians&quot;)

lose_dimw2 &lt;- dict_df %&gt;% 
  left_join(lose_dimw2, ., by = c(&quot;feature&quot;))

plot_lose_fw2 &lt;- lose_dimw2 %&gt;% 
  ggplot(aes(x = reorder(feature, frequency), y = frequency, fill = dimension)) +
  geom_col(width = 0.7, size = 0.2, color = &quot;black&quot;) +
  coord_flip() +
  scale_fill_manual(values = c(&quot;gray1&quot;, &quot;gray40&quot;, 
                               &quot;gray72&quot;),
                    na.value = &quot;gray99&quot;,
                    name = NULL,
                    na.translate = F) +
  annotate(geom = &quot;text&quot;, x = 1:20, y = 400, size = 2.5, color = &quot;gray25&quot;, 
           label = translations_lose, hjust = 1)+
  labs(x = NULL, y = &quot;Frequency&quot;, subtitle = &quot;Wave 2 - Dynamic: losing&quot;) +
  theme(legend.position = &quot;bottom&quot;,
         axis.text.y = element_text(size = 8))</code></pre>
<pre class="r"><code>plots_freq_high &lt;- arrangeGrob(plot_high_fw1, plot_high_fw2,
                          nrow = 2, heights = unit(c(9, 9), c(&quot;cm&quot;))
                         )

grid.arrange(plots_freq_high)</code></pre>
<p><img role="img" 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" width="768" /></p>
</div>
</div>
<div id="figure-a2" class="section level2">
<h2>Figure A2</h2>
<pre class="r"><code>plots_freq_low &lt;- arrangeGrob(plot_low_fw1, plot_low_fw2,
                          nrow = 2, heights = unit(c(9, 9), c(&quot;cm&quot;))
                         )

grid.arrange(plots_freq_low)</code></pre>
<p><img role="img" 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" width="768" /></p>
</div>
<div id="figure-a3" class="section level2">
<h2>Figure A3</h2>
<pre class="r"><code>plots_freq_win &lt;- arrangeGrob(plot_win_fw1, plot_win_fw2,
                          nrow = 2, heights = unit(c(9, 9), c(&quot;cm&quot;))
                         )

grid.arrange(plots_freq_win)</code></pre>
<p><img role="img" src="data:image/png;base64,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" width="768" /></p>
</div>
<div id="figure-a4" class="section level2">
<h2>Figure A4</h2>
<pre class="r"><code>plots_freq_lose &lt;- arrangeGrob(plot_lose_fw1, plot_lose_fw2,
                          nrow = 2, heights = unit(c(9, 9), c(&quot;cm&quot;))
                         )

grid.arrange(plots_freq_lose)</code></pre>
<p><img role="img" 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vZSA+AuIABwQ5IBxD3Zf2ad+B7te316g3ve32a3uv/iva/7r5K62ZBN6cpxzRnAa3rQPe92X39++nUNDODg4uYAsls3G5ykN3oOIL/ZtOEVAM8BAIAbogzgaDrWrVFpAdJT+2TERT0hm27bldc9aCZJs4dvCzSfECb/kKOZGTQYAsoCWsgA1Ec19yTwheZbz/l99bzxp+eZA0iD4UlgAGgOUQagpmfzHkzNs6Zd8S+StW/63vzF/ImqvO7Buq47p3kDuCD7kOyZ27pOtzagBQ3AbS0gnRtHc2Feq84AXkvCP/e/wwAAYPGIMoCahZgPWpmB9eqX6qEoew2GPFfwK59Rd8qki2YWaNIA3pTec59sz4fXazrdYkALG0BTq4FekAbx2pfepB4ESN43MABWAwUAF2QZgDgOdiE5WAuQVQAAmgADmI/4vL6lhPSdYaFbTAEASsEA5uNn5/fCkMrBmuwIAACLAgOYh1c+3RMdatzvn/P78fn/D85nCgAAmgADaMjBnrmr8mh6r9C5X164MgBAAgbQkKO906H+/AvmJihmgAGgCTAAAABPwQAAADwFAwAA8BQMAADAUzAAAABPwQAAADwFAwAA8BQMAADAUzAAAABPwQAAADwFAwAA8BQMAADAUzAAAABPOTsMYHq62xHMi+TokM4ZpHNGtnSyg6s2OgygA0iODumcQTpnZEsnOzgMoB7Zx5PoAwrpnEE6Z2RLJzs4DKAe2ceT6AMK6ZxBOmdkSyc7OAygHtnHk+gDCumcQTpnZEsnOzgMoB7Zx5PoAwrpnEE6Z2RLJzs4DKCe6T6AWtp+1InuKER3Y7Klkx2cVAOYG1v5nWgmvCp+eerO+I9+OT54d2VfMA/Tsn1M8gEluym2Ih0GIBfZ0skOTr4BnBxeE2EAFpIPKNlNEQNwR3J0sqVre3Cz2y49pP6LolfNm2LRvPvKNgCNMQANBqCQfLTLbooYgDuSo5MtXecM4LF3PYABVAAG4IzspogBuCM5OtnSdcQA9Iu9yx5odt/uG0Dc0397fxh+8IB+9+yGMLzs5sgaApoKY66yhoBODmsbeG5LUjOaWvnwlnDlgep+BQbgjOymiAG4Izk62dJhAPMS9/R/qfp41avPjYWa1YcbG0DS/0+ZmhvVy5U36l0splujr8X94eyDYwK6z+6lX7knCN66R71+6da1QfC2+OWRrcsn4v++vzUIghXqTVx2TRD8RlI0Pf3dXwuCJR+dUO++d02w5GPxzkfuWZt+TktUYQBheHM0Nx6uUb364M743H447uwbTQKfGtX9/0y46qEoOrUlvEXvVTsy1OKvorFDLRwT0H12L/1I3MsHS26bnv7pWvUqWHpviQGYsqRo+hFdMbg8fpe+mt4dpJ/TGpUYgLrX88zoyu/MjZme/OTw6sMNDODfmf4/nQ04M6pt46rmv3Y+GAJyRvbFOENA7kiOTrZ0VQa3N+j/fDR7XzCgXl55yLzMJoHVEJB5E6yPZr8WbNbvTo8s2xdFx0f09oFD0WS8y+kRNWr0eLBewhBQ1qWfGTUDOarzLzeAMBnrSceK9NspdRlQJRiAM7KbIgbgjuToZEtXqQH03x6pQf9lD5guXL+sM4DTIwNJ/WR64Mf3f+6KQFVSkwRqz9Mj/evufyqSMQdgDCDuxnMDGLy7gQEMbkmuF1IDiGthAHKQ3RQxAHckRydbukoNwEzzTvbffnxofZS8rDOAtCwxgONDerwnfVhA/51UW/qvPSTHABZ1BTB4t5kDzm8SjSIMQBCymyIG4I7k6GRL120DOD0SvPvabzw5UjCA6Mnt2hQmBBhAM3MAd8fdvXo7bnX6GIAcZDdFDMAdydHJlq7bQ0DHgs2RGfixDSDm1Sc29d8mwQAGd6q/je8CesthywDOjKr+fiYcvDne91tmLwxACrKbIgbgjuToZEtX7STwJfvmmQSO7SGZBL4+ip4YGkgMYOBQdGJTYQjo+NDAU1F0YpsIAxjckAzmlz4HEBtA8UGwGV11fzYFgAEIQnZTxADckRydbOmqvQK4Qo3cqIGgZGQ/6/PNwP6ANeqfFJ0eMTd/mkuF5Apgr9k2IGIO4K64N/+QeZCr/kngKHpmOD7PtxaDG9dbVc3BjWqvNhgAQC3VHmMlR53oXgwDcKbaOYBvbg+CdXoI50T8qv/Kp6y1gE7Hp/lP604+PuFX1wqms1dvLr5+r5ksSLbNfn1IbRR0F5AgZB9PNEV3JEeHdM7Ilq4Nk8DVgQGUIPt4oim6Izk6pHNGtnQYwLxgAM0iOTqkcwbpnJEtHQYwLxhAs0iODumcQTpnZEuHAfQaso8nmqI7kqNDOmdkSyc7OAygHl/uAmqHdKKPdtFtEemckS2d7OAwgHqm+37JBzAAUSCdM7Klkx1cew1gsTkcrbSP89GxlJDd7ps7AgYgCqRzRrZ0nQnu9KYg2KyywjcHBlACBuAuneimSC/mjuToZEvXmeD2qid/VVb45sAASsAA3KUT3RTpxdyRHJ1s6ToSnFnqv/mbhDCAEjAAd+lEN0V6MXckRydbug4ZgFrlQaIBPDMcrtqpXp/aoRZ9++ABvfmuHWG6+YYw3Pj88JrkeQCz2rMxhOe2JAsDRVMrH94SrjygDSDJCl9XWN2vwADcpRPdFOnF3JEcnWzpqgxOLevzL/QaPwOTwbJ90eNX6LQu0TG1stt/uU0t7xbN3jcUBL8VV4r2Xvr09qD/umh2e6CWDk2WD7rWrAf0o02qqO0G8CdqzU7Tb5v1O9Xr8fyl2fwHcYev13SbG9M5H2dMsnfNxkj18TeqNUKVAST9f32h/b0tZjr2xABaTQgNAJ3ESvZ+0dpg+USW7v0n6n//XGeFt/K9717+5+rlH25NMsEneeRXTNvp4VtjIQNI1vrX6Rw/HPfRp0aT1I5JCoD4z8bD0bNqvU991h/7gVrhWfX0M+Gqh+IdthgvMOuADt59ymSFLynEAJoEAwDoJeJu+/LpI38VfNy8mn756qV74m796uUT8Yb4z/Tu2BteVm+nv6vKdwfL90z//Vr197uxXcTv3xHXvMfsv2Ji+hHlBa2xoAFcpXvrfIR/yhhAulkl/4rMwM/cWPxyZvDfxn26zg2ZDPifGV2T7KUM4N+NZgkkawurwxcDqFY1I53oi3HGMdyRHJ1s6aoLzs70lYz2J+nerTmALN97kj/MVE2ywJh9B6IsPXybU0KafjqZ4p174cE7bgjzdf3V5sQbdA21dXzlw6Mm+0uaHEaP7iRL/o+HyVhPWWF1YADu0oluivRi7kiOTrZ01QVXk+w9ytO925PAab735K2dBsyM+9i5wTpnAHM7ki67xgD02buuEb+eG1ujLgSS1I9hlvUrM4DBLUkG4frC6sAA3KUT3RTpxdyRHJ1s6dpnAHm698JdQEm+90MNDcDKDdY5A4g770/e+eCLU42vAM6Mrjk5fFU0tfLbY2lOyJTUAAbvNnPAZYXVgQG4Sye6KdKLuSM5OtnStWcISHXgebr32ttAVb732+sNIPWPqPMGoIf1o3RCONtszQHEm1f+Bz35+6fD6ix/3OrXcwNIq9YXVgcG4C6d6KZIL+aO5OhkS1ddcMVk78oA0nTvqQHEvX6W773OAGa39V8Xv3w827+zBjC4U9+2UzSAuCdfczh6blj36jODG+IT+zOjYXKjz+DNsWN8K1xTMIC4+JbSwurAANylE90U6cXckRydbOkqDK6Q7F0PASVDOskGlRU+y/deNwdQk0e+40NACWsKm/PnAPTrNfoiwYzv7M9G+W0DiPt+taW+sDpYDtpdOtFNkV7MHcnRyZau6gfBsmTvUZ7uPXUEdTWQ5nuvNwD9IFiwbl83hoCiuf1xV3/ZTc8PZ3d4mpuDTu0Iw43mpRkfUhcFZu9nN4Th4EYzRJQbQHITaV1hdcg+nmiK7kiODumckS2d7ODIB1CP7ONJ9AGFdM4gnTOypZMdHAZQj+zjSfQBhXTOIJ0zsqWTHRwGUI/s40n0AYV0ziCdM7Klkx0cBlCP7ONJ9AGFdM4gnTOypZMdHAZQj+zjSfQBhXTOIJ0zsqWTHRwGUM9ZfhtoW6UTfbSLbotI54xs6WQH1xsGoJd+mGm46mfjEifO7gfBMACZIJ0zsqWTHRwGUA8G4C6d6KNddFtEOmdkS9f14MwyEPrJsTp6yQA6BgbgLl3Xj/Z5kRwd0jkjW7quB4cBNAkG4C5d14/2eZEcHdI5I1u6rgfXqPNXSDOAuKf/9v4kdbxZ7kEnfs+GgGbypAJWWniGgJoAA5AJ0jkjW7quB9djBvCXab74NO1XmhygxgCstPB1BtBiosuz2wBaTQMKADLZvfQr9wTBW/eo1y/dujYI3rZnWucLNjmDX7omCH4jKQyCJR+daPkLqzeAMLw5mhs3yzyrFaSfG9b5Ie1J4CnlCXZaeAygCTAAgLOU3Us/ohZ8XnLb9PRP1+q1n5femxuA2RRvSQtbzgnfDgMwKR9XfmduzCR+VzljCgZg1oO208IzBNQEDAHJBOmckS1dJ4PbG/R/Ppq9TycDCK48ZF6mk8Cz24L10ezXVBqxvcF7dOFmeUNApl+Pu/ckh5g++7cNIE0KaaWFxwCaAAOQCdI5I1u6jhpA/+2RGvRf9oBOFGlepgaQZ5Q8PqRfzW4bkGoAU+EtuQEM3m0ZgMkIVkwLjwE0AQYgE6RzRrZ0HTUAkxR4sv/2NAFw/DI1gDwncJYevt0JYZpmwSuA+Mx/Tbo12wsDaAIMQCZI54xs6SQbwLJ7xRnAAnMAegJYYaeFxwCaAAOQCdI5I1s6kUNAqRXIGwJSt/6Y0/yyu4BmwvTE304LjwE0AQYgE6RzRrZ0nZ0EvmTffJPA10fRE0NqS/91sT08PiRxDmBDmuy95DmAfOp3jZ0WHgNoAgxAJkjnjGzpOnsFcIUe2XkgG+ZJEsbrh8HMJnWRkBUKNIC74p79Q2aYp+5JYNsArLTwlRvAWU2VUtVJJ7op0ou5Izk62dJ1dg7gm9uDYJ1+8PdE/Kr/yqfstYBObArUJUJSGKzbJ3AIyAz8dxXZxxNN0R3J0SGdM7Kl68Ik8OLBAEqQfTzRFN2RHB3SOSNbOgygCTCAhZEcHdI5g3TOyJYOA2gCDGBhJEeHdM4gnTOypcMAeg3ZxxNN0R3J0SGdM7Klkx0cBlDPWXMXUBekE320i26LSOeMbOlkByfRAOryf413dlzobHkOAAOoRXJ0SOeMbOlkB4cB1IMBuEsn+mgX3RaRzhnZ0rUpuFcXrDFfJrAUDKAEDMBdOtFNkV7MHcnRyZauPcE99q4Fp3sxAEcwAHfpRDdFejF3JEcnW7r2BLeI+3163QDypO+xATwzHK7aGb+cWvnwlnDlAbs021QVGIC7dKKbIr2YO5Kjky0dBtAsxgCspO/jg3+SZoqfWnmjXh7OKk03WbSY6PJsMYCWM34CgECObDUZfa3c7t/9tfRlmvr9yNbl398aLPmYKs0qHtm64pFg6Z4j96xNk8e3RPsMwE76Pp6sEa3zAejhILs02YQB1IIBAJyVJAZg5XZ/xGR4uXw6T/1+ZOuvXlPYpioe2XrR2mD5xO4gTR7fGu0zADvp+7he7XPGJAnQC3/apVPVrgXKEFAr0om+GGccwx3J0cmWro1DQFlu9+j0yLJ9UXR8xE79Hr+6dF/0mEkOkFbUxZFJF/N4sF7sEFAh6bvp7k8O697+lqgmJfyUlRusEjAAd+lEN0V6MXckRydbuvYZQJ7bXf3vx/d/7orAzvs1u00t/a/6equiShGmNvavu/+pSPAcQCHpe50BFEoxgAZgALVIjg7pnJEtXTsNIM3tniZ7CezMv2YS2GQDziomM8OTOh/MtYfEGkDhVtCSKwCrFANoAAZQi+TokM4Z2dJ1wABUCuDg3dd+48mRBQzAJAhWhU9u14YwIdUACknf6wygUIoBNAADqEVydEjnjGzp2mkAaV8fHQs2R2a4xx4Cyg0gq2jdG/rqE5v6bxNrAHbS93oDsEsxgAZgALVIjg7pnJEtXZsMQCf7TXO7xwYwcEhlfCymfk8NwKpoNh4fGngqik5sE2wAdtL3egOwSzGABmAAtUiODumckS1de4KbDIIBK/H76ZF0jCdP/Z4bQE2G+EjdFqQZEDsHENlJ30sMwCptgwGcJVQry6KkE90U6cXckRydbOnaE9xpdbaf5XY3+d4vvl5dF2Sp3y0DyCsmBjD79SFVX+ZdQN1G9vFEU3RHcnRI54xs6WQHhwHUI/t4En1AIZ0zSOeMbOlkB4cB1CP7eBJ9QCGdM0jnjGzpZAeHAdQj+3gSfUAhnTNI54xs6WQHhwHUI/t4En1AIZ0zSOeMbOlkB4cB1NNLdwF1W6saZDdF0W0R6ZyRLZ3s4DCAenroOQBpgstuiqLbItI5I1s62cFJM4C5sfL0jzN6zedTd7b8BYsAA3BGdlMU3RaRzhnZ0nUiuIUzxNeQrgjRWwagHwVrPxiAM7KbIr2YO5Kjky1dB4JbRIb4GnrOADQYQC0YQFNIjg7pnJEtXQeCW0SC4BowgHnAAJyR3RTpxdyRHJ1s6TCAeTi1Y0MYhh88EKnc7g9vCVc+NDZ41/5kSxQ9tyUML7tZvVJDQDoR/FWFzeODd+0Iw1U7K/wNGEALyG6K9GLuSI5OtnRtD252m1rWTa33M5QsDBSlPfwxlf9Rv8wKZ7cNTAbL/lZvG1m2p7sGcHLYLOqpzvqnVt4Yhqv/aWxwQ7rF9PhhuDEqGoC1eTzf36bFTMc9ZACtJnUGgN6mmCF+6b1m6yMq4/tulQf+5asvtwpNVvjvb10+8fLVXU8KPx5++HB8GTBqunXVjc+NhYM7o7lxtdD/TLjqobh0i1rt05oEtjeP69pjYc3QUIu/CgMAgJ5ht+r1dwdvn5g+co/q8hU/CT4ed/a/HpeoV3lhbBeXK9OIPaD1/r9VA0iYMgagRnfiztwk/c1SAcev1xQMwN48rneaqTWA1uglA6jyd1eA7ItxxjHckRydbOk6NAegsoFFUZL0PVLZwVS+gD9+5+2q1Co0FWa3XbJJLR3d7TmAaDsJ5J4AACAASURBVO6FB++4IRnYsbP9joe3xF6QsPqwZQCFzVamgOrAAJyR3RTpxdyRHJ1s6TpkAGnax0nVr+uNlx6aXPZ3I5uNE2SFaZaAIFDzA102gLkdSV9eZwDxuzOjaU+vM0SmBlDYjAHIQnZTpBdzR3J0sqXrlgEcW/ZA7AF7B44PbS4zgP6/GIlrdtkAxsPBT9754ItTJVcAg3enLzX2FYC1GQOQheymSC/mjuToZEvXpSGguNP/xMj6aPLSrxUL8zxhx4KBLhvAmdHVKqfj3JhtANYcgJXr0Z4DsDZjALKQ3RTpxdyRHJ1s6TphAOqsf29w5aFo9j51Q6hmdttvvvP26Fj/FWpDXminig82d9sABnfq+3kKBhBvOzVm7gIavDne8q0wnwR+y+HiZgxAFrKbIr2YO5Kjky1dB4KryRCfbdXn/sHmKKrLCp+kir90ottDQAlrrCGgG5Px/Sjan431pwagrcLa3CYD6B2q/N0VILsp0ou5Izk62dJ1ILg8Q3z/lU9lW48PDejBfu0IWaGdKn5vcHmXJ4H3x136ZTc9P7z6cG4A3447+A8d1uXPbgjDwY3qtVkN9JlhfcNnvrk9BiD6eKIpuiM5OqRzRrZ0soPr9m2gEpF9PIk+oJDOGaRzRrZ0soPDAOqRfTyJPqCQzhmkc0a2dLKDwwDqkX08iT6gkM4ZpHNGtnSyg8MA6pF9PIk+oJDOGaRzRrZ0soPDAOqRfTyJPqCQzhmkc0a2dLKDwwDq6ZnbQLstVD2ym6Lotoh0zsiWTnZwEg2g24nhe+VBMAygWSRHh3TOyJZOdnA9ZwAdSAuJATgjuymKbotI54xs6boXXJr3cR56ygA0GEAGBtAskqNDOmdkS4cBNAsGsDgwgGaRHB3SOSNbOgxgAVpNDG92OlDRT4gwgBaQ3RTpxdyRHJ1s6TCA+Wk5MbzZ6bD9mS0muuwVA2g5oycA9C5Hti6fUNl/L09evnTr2iB42x71bsUjwdL/XW+7emm84aVbg2DJRyfSkj1H7olrvnVPyxFUYAAtJ4Y3OxVo8VdhAAAgn0dUXvfdKg/8y1dfPv3TtXrN56X3xt38RWuD5d+PDeDlq1WVpGTFdFIysVu/bz0rfAUGkOCeGN7sVCE9YwDV/uwqkH0xzjiGO5Kjky1dO4M7FmxWiV+WPaBf2VlfVMLf2W2XPm1Wg94bvEeXbE5KTIawx4P1AoaAWk4MP2VlCKsEDMAZ2U2RXswdydHJlq6dwZmE73/8zttVVshC3keVGHJ22yWbdP+vMwOo9wNJysjTI/3r7leZAwQYQMuJ4TEAOchuivRi7kiOTrZ0bQ1u76WHJpf93chm4wR1md+DQJ3vp/nAgkDlgdQuManzg117SIABtJwYHgOQg+ymSC/mjuToZEvX1uCOLXsg9oC9A8eHNpcZQP9fjKhLgMwA0pzwUfTkdm0IXU4JGVWRGB4DkIPspkgv5o7k6GRL19bgjg99YmR9NHnp13T6X2sIKM37eEwliU+tISrcGvrqE5v6bxNgAK0mhscA5CC7KdKLuSM5OtnStTW42W2/+c7bo2P9V6hBfnsSODWA2W165rf/OjXpO5SWHB8aeCqKTmwTYACtJ4bHAOQguynSi7kjOTrZ0rU3uMlAn/vHvXw20tN/u535/fiQ/lMsib1CMyBgDqDlxPBtMIAeodqfXQWymyK9mDuSo5MtXXuD0zf4zJqbPaMT2+NO/sqnItsA4r5+vSkJ1u3LhoBmvx5bwsXXS7gLSB6yjyeaojuSo0M6Z2RLJzs4DKAe2ceT6AMK6ZxBOmdkSyc7OAygHtnHk+gDCumcQTpnZEsnOzgMoB7Zx5PoAwrpnEE6Z2RLJzs4DKAe2ceT6AMK6ZxBOmdkSyc7OAygHoF3ARXC65Yui0B2U0Q6dyRHJ1s62cFhAPXIew4AA6gGydEhnTOypZMdXI8YwLx54qNTd1b6ZRiAM7KbItK5Izk62dKJDC5dEeKsMICq0wRjAM7IbopI547k6GRLJzK4s8UANBiAGGQ3RaRzR3J0sqUTGRwGMA8YgDOymyLSuSM5OtnSiQxOuAGc2qGywn/wQKRSvj+8JVz50NjgXfuTLVH03JYwvOxm9crOE18ZGIAzspsi0rkjOTrZ0nUnONPDH0syQx6KTmwfShcDGpgMlv2t3jaybI9IA1ArfirUWf+UWhl09T+NDW5It5gePww3Rg0NoMVMx/IMoNXczQDgFYvNF98a7TGA8fDDh+PLgFGTIkB1+iZFwNy4SQuw6iGdQeCWhpPALf4qDAAAepqfBB+PO/tfj7t89Wp38PaJ6SP3xH5wZGtw+XT8N/aA1vv/NhlAQpIlUp3cF5KEmemAM6NruAtIGrIvxpHOHcnRyZauS8EtLl+80DmAaO6FB++4oSRP/Hh4S+wFCasPYwDCkN0Ukc4dydHJlq5bwS0qX7xMA5jbkXTxdQYQvzszmhpAvAkDkIXspoh07kiOTrZ03QpuUfniZRrAeDj4yTsffHGq5Apg8O70pQYDkIXspoh07kiOTrZ03QpuUfniRRrAmdHVKtXj3FghT3w+B2ClgMQAZCG7KSKdO5Kjky1dt4JbVL54oQYwuFPf5lMwgHjbqTFzF9DgzfGWb4X5JPBbDlfyxQkYgDOymyLSuSM5OtnSdS24xeSLn5BoAPE5fsIaawjoxmTYP4r2Z1MAqQFU/SCYOArhVfhTq0Z2U0Q6dyRHJ1u6rgW3mHzxl4s0gLn9cZd+2U3PD68+nBvAt+N+/0PmTP/ZDWE4uFG9NquBPjMcVjkIJPt4oim6Izk6pHNGtnSygxNpAF1G9vEk+oBCOmeQzhnZ0skODgOoR/bxJPqAQjpnkM4Z2dLJDg4DqEf28ST6gEI6Z5DOGdnSyQ4OA6hH9vEk+oBCOmeQzhnZ0skODgOoR/bxJPqAQjpnkM4Z2dLJDg4DqEfabaC14XVFlMUhuykinTuSo5Mtnezg5BiAXuJhpnBD/0zt7f0VZ39vgLAHwTCAypAcHdI5I1s62cH1lAFUveZDAzAAZ2Q3RaRzR3J0sqVrZ3BmRQeT3dEJaQYwPxiADq8TGjgiuykinTuSo5MtHQawODCABmAAlSE5OqRzRrZ07TaAlj6gGwaQLOuQJHhXyzroBO/ZENBMlgzADAHphR9uOhzlyX/zpPDjg3ftCMNVOyv9FRiAM7KbItK5Izk62dJhADXEPf1fpgne0/Reqw83NICprEZqAFZS+PE8e3zxZ7WEMANoOXEnAPQ4u5d+5Z4geOse9fqlW9cGwdv26NS+E+q/eNM1QfAbSWEQLPnohNpl+fdvDZZ8bPpIvOXtE3ZRvM/3rlFFreJmAGF4c5LgfUqt8hw9Nxz368VJ4Kk04eOZUdW7nxpTf80QkJ0Uflznih+rXQuuxV+FAQCAKHYv/Yha0Fklcv/pWr2289J7cwMwm+ItaeEKtcvyP1cv/3Cr+nu5XXREb9IbW8PRAFQvr5K7zI2Zc/eTw6sPFwxgJlvs2SQHMBgDsJPCj+v6M5UuBsoQkDuyL8aRzh3J0cmWrqLg9gb9nzcpXWqyuyS5XdZHs19TS//vDd6jC/XLS/dFTwypv48Hlx6yiuL6A4eiyWCgO0NApgePO/Ik95c++7cN4OSwrqLfqVGey2560Wxek48a6VEhYwYVTw5jAM7IbopI547k6GRLV5UB6HX9VVLHmvyO6j+V+N2gkwCowoFkF1XNTBZYRWZj/EHdSAiTGoBO8J4aQJLrdya5ODBZH/W7OZP/ZdV3ko6+kBQeA5CF7KaIdO5Ijk62dFUZgOqydVr3mgzvpm9fn1RLUn8F+ow/7fpTA8iKso3dNICGVwDZmH46IPTCHRt0drDkCsC6XRQDkIXspoh07kiOTrZ0XTKAuHojA7Ayw3fHABaYA9ATwArrSeCTyiqSOQArKTwGIAvZTRHp3JEcnWzpOj0EtD7fpdYAsqJuG0B2607ZXUBmAlih3p0cXqUeGHg21Aagsr/bSeExAFnIbopI547k6GRLV9kk8CX75psEvj6KnhhSW/qvi/v2x9V4f40BWEXdHgLakCZ1L3kOIJ/kXZNNAie3+ifZ362k8BiALGQ3RaRzR3J0sqWr7ArgimRkJx3miS8JsqUgzCZ1kZAX1hpAzX7dNIC7igneC08C1xqArhF+SD83nGR/z5PCt8kAZFEbXpW/tWJkN0Wkc0dydLKlq2wO4Jvbg2CdfvD3RPyq/8qn7LWATmwK1CVCUhis2xfVG0Be1G0DuHvhWl1E9vFEU3RHcnRI54xs6aqdBK6Ybj4HIBbZxxNN0R3J0SGdM7KlwwBqwABaQ3J0SOcM0jkjWzoMoAYMoDUkR4d0ziCdM7KlwwB6DdnHE03RHcnRIZ0zsqWTHRwGUI+Qu4AahtdJLZpEdlNEOnckRydbOtnBYQD1TIv4GRhA9UiODumckS2d7OB6xABO3dmuTy4BA3BGdlNEOnckRydbus4F55AfrEcMoEPJgBMwAGdkN0Wkc0dydLKlwwBaBwOwkHy0y26KSOeO5OhkSyc7OAygHgzAGdlNEenckRydbOlkByfFAKZWPrwlXHkgip7bkqwNFI0P3rUjDFftjNJs8PmGyK6X7VoVGIAzspsi0rkjOTrZ0rUc3N5l37wvCH4rW+in/1qzjs+PNgX918XbZu8bMsXpgj9D2YI/aRV7v4HJYNm+dB9BBnCjXhN0yqwDtzGylwFNDSBfF9Sul+5q0WKmYxFp2EUEAQBdpj4dfDGt++4gKbbSw5uM8VkVe7+L1gbLJ7J9WqQ6AwiTLMCrHoqiU1tUzpfxPG1Amgkm22DXS3bFAADg7GN3sOR/mj5yj+q+dwfvmFAvP6569xUT04/EG1++Ou72p78b9/PaAHYHb58wtfMqxf1iQ8j3aZEKDUAn/zLLO0dnRlWyF71pxjaAbINdbyrPG1YNDAE5I/tiHOnckRydbOlaHwLKcoGVpXU/dHqkf939T5nt6p2VLiytUrJfso+cISCd5zHPBbD6sL3Wf2IA2YZCvSkrRWQlYADOyG6KSOeO5OhkS1fBHECeDbg+rfuhaFJnebn2UCHvY5IwuEE6+GwfYQZwZjTt2Fd+Zz4DKNTDAOQguykinTuSo5MtXTsMwErrHv99cnveuc9vAOl+2T7dSAhTSnoFkKYDjqL5rwCsehiAHGQ3RaRzR3J0sqWrdAioPq27efTr1Sc2mR6/JmN8g3Tw2T63yTKAaNzqzOcxgEI9DEAOspsi0rkjOTrZ0rVuAFk6+LK07mqA/6koOrEtOeUvZoxvkA4+20eaAcyEgzfHFwLfyid6EwN4y+HCBrseBiAH2U0R6dyRHJ1s6Sq4AmiQDj45n99rhncGGmZ+r9+Y7SNsDiCK9mdD+0UDMA+C5RngrXptMAARNAyv2l9bKbKbItK5Izk62dJV8SBYIR18Ma17fLr/9bh7v/h6O/N7ljG+QTr4bB9xBhA9uyEMBzeqx7rs/v6Z4fhc395g1aveAEQfTzRFdyRHh3TOyJauqkngtiDGACQh+3iiKbojOTqkc0a2dBhAryH7eKIpuiM5OqRzRrZ0GECvIft4oim6Izk6pHNGtnQYQK8h+3iiKbojOTqkc0a2dLKDwwDq6d5dQIsLr80/vxVkN0Wkc0dydLKlkx0cBlBP154DwADajOTokM4Z2dLJDk66AZhFHzqaEx4DcEd2U0Q6dyRHJ1u6yoJ7dZFZf61KyTKgDculGsDc2ODdZ0ZVkhdtAJ3NCIkBuCO7KSKdO5Kjky1dVcE99q4HMICOgAE4I7spIp07kqOTLV1VwambgXwxgBwMoA7JR7vspoh07kiOTrZ0GEDznNqxIQzDDx4wBpCkBC5NBJ9vqg4MwBnZTRHp3JEcnWzpqgludptauy3uu5/eVpvnPcHOF69W//mjILjyR9oAajLC/21cfizYHKnEYCtEGoBa+y1J/24bQEkieGuTTYuJLruVjpc0wABQhs7wvuLI1l+9pi7Pu8HO+57lhr9o7Yr6jPDfj8tfvlrt+JPg4y2G1R4DGA8/fDi+DBiNe31rCKgkEby9CQMAgLOW3UvvVTawfM/0/1PM854UW3nfl0/EFeO3f7+2LCO8yR0ff5r50xLtMYCEqaIBlCSCtzdVB0NAzsi+GEc6dyRHJ1u6aucAVHawmjzvmkLe96z4WDAQ1WWE10NEagzo9IicfAA1zL3w4B03hAUDKEkEX9hUHRiAM7KbItK5Izk62dJVPwlck+ddl9bkfU+SQMZdfG1JYgCqJDYBmQYwtyPp120DKEkEX9hUHRiAM7KbItK5Izk62dK12QCSleJq8r6XGUAxk/zksgfiT5RpAOPh4CfvfPDFqZorgLpE8IVN1YEBOCO7KSKdO5Kjky1duwxgfaG0Ju97YQioNJP88aFPxO4g0gDMI2BqgKc4B1CfCH686mRgGgzAGdlNEenckRydbOkqM4BCml8rz7umNu/73uA9h6Inhhplkld/Lx7aLNUABnfqe3tyA1A54UsSwdubqgMDcEZ2U0Q6dyRHJ1u6qoKbNM8B1Od5N9TkfTdv3638oTRNvPq8+JJCpAHEJ/YJa1ID0M8BlCSC39+GKQCWg3ZHdlNEOnckRydbuqqCO70puPTpsjzvCTV539Vb60Gw2kzyyX1DMg1gbn/c41920/PDqw+bYX6dE740EXy+qTpkH080RXckR4d0zsiWTmpw+mFgmQbQZWQfT2IPKAXSOYN0zsiWTmhwJ/R1AAZQguzjSeoBpUE6Z5DOGdnSiQzu9EgQqJuDMIASZB9PMg+oBKRzBumckS2dyOBmtwVXqv9jACXIPp5kHlAJSOcM0jkjWzrZwWEA9cg+nkQfUEjnDNI5I1s62cFhAPV04zbQZsJr2w9vHdlNEenckRydbOlkB9cVA1AZH8u2z+g1PmsywLdpvYd5mO77pU6DAXQEydEhnTOypZMdnDwDqM3/iAHUhte2H946spsi0rkjOTrZ0jUM7tVidsaqWPxnHh/q0nLQjQxAgwEsFF7bfnjryG6KSOeO5OhkS9couMfetcgcv02CAbQKBuCM7KaIdO5Ijk62dI2CW3SS9yaRZwB5ivckmftDY4N37U+22Hnd1RBQmgBYr/Fw0+HMANT7JPt7VhS1Kyk8BuCI7KaIdO5Ijk62dN4bgJXiPUnm/k9jgxvSLXZed8sAprJMX9oA0txfap3ovChqV1L4zhtAixEDQC+RJnlf/v2twZKPqS0v3RoESz46kZUvn5hO0vsu/941dXV2L//+rWrjkXjL2ydUet+v3BMEb92T7vnSrWuD4G170sTvOgm89Q0v/ct4t++tXVEaWxMsxgCsFO9JMve4Ox/cGc2NqxXe7Lzu+STwmVFV75QaKtIGMKV2iJ4bjitYRW1LCo8BAEAbSQ3gV69RSzVfPj3907V60eYVWXluACahV7HO7uV/rl7+4dakaPfSj6hXS24zeyY1l95run5tA9be5uVFnTGAhCljAOpGz9gATEbHuGu387oXDGBnsp8ygHTS4OTw6sNWUduSwnfeAJoJr8rfWjGyL8aRzh3J0cmWbv4hoODSfdFjwUBkMrjM3qeW6FTYqV+CgUPRZE2dvWrHJ4bU38cDnQCm//OqKMsHc+Uh81Z/j/6T7x1/ZJIupjOTwFmK95pUjuPhLYW87tZtoColwGU3vZhUTnKEmR3tojYlhccAHJHdFJHOHcnRyZZufgNQCV1Oj2Q5HGe3ZRm+cgNQPXhtHZUfLDJFpsfXmWHUBvVW1U7e6mWfTW7gbO88YWQnDMBK8V5jAPG7Ql53ywDmTKqXVd+pMQA1JpQVtS0pPAbgiOymiHTuSI5OtnQLTwIXkrwnM7i2AZTV2Zt1/YkBmOTwk/232xmD47e681cmYO2dp4zvhAFYKd5rrwAG7y7c5Fl8EOyFOzakOcEKVwDFokrjN2AAzshuikjnjuToZEvXpAGYjnx+A1DjOYs2gGhy2QOq1Nq7owZgp3jPDMCaA7AmcOueBD45anKC2XMAVlHbksJjAI7IbopI547k6GRLt1gDWF8ozQd+yuvUGUDDIaC45//EiE4NnO3d0SEgO8V7bgDxtlNj5i6gPK97YgBvORz/WaUeEng2TJJCWncBWUVtSwqPATgiuykinTuSo5MtXUMDKGRpn93Wf1388vGhgbQ4WK/niA+V16kzgOCSfeWTwPGGi4c2q/9Ze3dyEthK8Z4PAd2Yjdxbed0TA1BWMZ49O1D3HEBe1Lak8BiAI7KbItK5Izk62dI1Cm4yCAasHjwZodEn8opj6t0lm2wDsOvUXwFckQwO1X/apBlXsraZl+/uzINgVor33AC+HXfdHzLDOXled7MaqMkAr7aGHzrQ6ElgXRS1KSk8y0G7IrspIp07kqOTLV2j4E5vCi59Ou/BoxPb4z553b6s/LGhYN3ThTkAu079HMA347J1hZr9Vz6lPykZ8bG/Qb288kfdWgtIOLKPJ5qiO5KjQzpnZEvXmeCSSeBSjqUPF9SDAZQg+3iiKbojOTqkc0a2dF03gBPzLA6EAZQg+3iiKbojOTqkc0a2dF02gNMjQbC+vCjCAEqRfTzRFN2RHB3SOSNbui4bwOy24MrGu2EAJcg+nmiK7kiODumckS2d7OAwgHo6dxeQW3gV/9wqkd0Ukc4dydHJlk52cBhAPR17DgAD6DCSo0M6Z2RLJzs4CQYwb4bIRpy60+m7FgMG4Izspoh07kiOTrZ0LQVXSO+VrwzUEq9ar3vWAGozB1cJBuCM7KaIdO5Ijk62dNIMQCWjz8AASsAAnJHdFJHOHcnRyZau2uBaN4DC/UIYQAkYgDOymyLSuSM5OtnSYQALERvAXfvD8INmdZ/ntmSL/pzaoVb9+eCBbPWgjTq7zMbDaer4Qu3KwACckd0Ukc4dydHJls4puBObguBf7DP9vVnp+fTIpU8XhoBm7xsKgt/KVvbpv1avAzQwGSzblO5wKHr8ClMS7/ajTUH/deqxgEAtGZrsLMUANmTre06Z9T03RmZd0HxJUL1+6M1jpudPDcCqbdNipuOOGUCrKZkB4CzEJG1feq/O8K4Suuu07iZFvEoWr9gdJFngrWzvR7ZetDZY/t10h+lH0nzyWWZ5k4ze2rk1KjIAtdr/3LhJCbDqIZ094Ba17vOHD8evR+OePq6y8kD0bBiuPhA9oxaFNkNAdm0MAADOBuJeOu6z/yruwVV///LVcdc+vXvpvQUDePlq9eK7ccW4N3/HxPSRe3R19T7b4eWrl+6J/eFqtVuwYiL2gxV6u71za1RlAHmCMDMdcGY0H+KfMgZwi84tc3dSL0kdX1+7AhgCckb2xTjSuSM5OtnSOQSn0vga9IjP5LIH9KbCXUCnR/rX3a+Xe7ayvZsUYNkOMT++/3NXBHZmeT0HkO0sZAgoSREc3pImfjGpX6K5Fx6844ZQG4Dq6k3F3ABqalcFBuCM7KaIdO5Ijk62dA7B5dkbk+wum/WSzsXbQCd1dpdrD9nZ3pPSdIe0qJBUQE8CpzuLMoCp8JYzo2mXrtKA7UheX5VUqTWAQu3qwACckd0Ukc4dydHJlq51A4jP7ZP/F54DeHK76dytbO9JabrD6ZHg3dd+48mRegNId56QZADjg3enLzXj4eAn73zwxamGBlCoXR0YgDOymyLSuSM5OtnStT4EFE0u+7+UJdQ/CPbqE5v6b7eyvaelyQ4mE8zpMgNIdr5NhgFYcwD5fO6ZUTMMNNbQAOzaFYIBOCO7KSKdO5Kjky2dQ3Cz24Lro+iJoSTDe3xF8N+qOzsLBnB8aOApleyl/3Yr23tamuxwLBg4pO4oLRpAXJLtLMUABndGp8bMXUCDN8dbvhW/PjMab1W3+JQbwFsOF2pXCAbgjOymiHTuSI5OtnQuwZlRnbinTjvuIO2/8yuAvWbcZ8DO9p6WJjuoZDD22JCZUlb7pDtLmQO4MRvI358P6o+nI/xrygxAPwewvw1TACwH7Y7spoh07kiOTrZ0zg+CXbIvG9OZ1Em9igYw+/W437/4el07zfaelZod9MdcfP1edZWQGcBpfUWQ7CzFANRjvh8yd/I8uyEMBzfqwZ/9cS9/2U3PD68+XGcA0TPD+rQ/r10dso8nmqI7kqNDOmdkSyc7OAkGIA3Zx5PoAwrpnEE6Z2RLJzs4DKAe2ceT6AMK6ZxBOmdkSyc7OAygHtnHk+gDCumcQTpnZEsnOzgMoB7Zx5PoAwrpnEE6Z2RLJzs4DKAe2ceT6AMK6ZxBOmdkSyc7OAygnrbfBtpieNX8yrYguykinTuSo5MtnezgZBlAE7nB1ANia6q9/TOl3Q+CYQDdQnJ0SOeMbOlkB9erBqAXAm1TVkgMwBnZTRHp3JEcnWzpuhXcq4vJH9mzBnBmdGXTeYQXCwbgjOymiHTuSI5OtnRdCu6xdz1wdhtApSkACmAAzshuikjnjuToZEvXpeAKud8bIs8AStPDT618eEu48oBJAKYWAp1KVwDqxaTwGEC3kBwd0jkjWzoMYPE0Sg8/pVaLW3243gB6Mik8qYABYEFeunVtELxtj355TRD8RuGVSQ6s/+5e+pV7guCtuvi7vxYESz46YXK/mzrZx8Rvv3dNsORjcbUj96xN92iJyg2gPD38lLGE3ACSIaDeTAqPAQDAQvx0rV6yeem96cvCq4IBfERtXHLb9PQjZpnnyy0DyD9Gb1SlKo98ukdrVG8ApenhVVKwMgPozaTwDAF1C8nRIZ0zsqVzDm5vcOWhaPa+QOV5CdZHs1/TaYHzV3mKl6D/86bi6ZFl+6LoeJr7PSm1PmZA5QMYMNnho8eD9eKGgMrTw0+ZU/xaA+jRpPAYQLeQHB3SOSNbOtfgTCcd99rLHshTRNYmi8xyfJmK8f9+fP/nrggsA7A+xtRQG06P9K+7/6lI4BxAeXr4BgbQo0nhMYBuITk6pHNGtnSuwaWpfift46wKAgAAIABJREFUrL816eILWX4nda7HwGSKzwzA+phiWrAg6L/2kFADqE0P3/AKoCeTwmMA3UJydEjnjGzpOmkAp0eCd1/7jSdHFjSA6Mnt2igmpBlAaXr4zAD0/6bCfA6gJ5PCYwDdQnJ0SOeMbOnaNgSUjudYQ0DHgs3pnqVDQLkBxLz6xCYZSeFzGqSHTw1gSo31PzccWncB9WJSeAygW0iODumckS1dNZPA10fRE0P2q7h4fZL8fa9KH6wrHlOzvCc2BYkr1E0CpwZwfGjgqSg6sU2eAZSnh08MQGWCD8M/yO8CaldSeAzAEdlNEenckRydbOmcg0vG89XpvXlZfHVMvbhkk+7rr1Cv41P+0yPmRs/45WQQDOgeP/+Ywo1DmgF5cwCl6eETA4hObgkHP2XdBtqmpPAsB+2K7KaIdO5Ijk62dO7Bndge99tXPqVfbgrUaX7h1WNDwbqnzRzAN+Oa6w4lpRdfr87+T8fXAbo0/xjLAGa/PqQqSrsLSAayjyeaojuSo0M6Z2RL1/7gFrfqQykYQAmyjyeaojuSo0M6Z2RLhwH0GrKPJ5qiO5KjQzpnZEuHAfQaso8nmqI7kqNDOmdkS4cB9BqyjyeaojuSo0M6Z2RLJzs4DKCe9t0FVE14lXxKe5DdFJHOHcnRyZZOdnAYQD3T7foZGECXkRwd0jkjWzrZwXXbAGbMQs8qsdfgpxZRMWPcSh556s6mv3c+MABnZDdFpHNHcnSypasyuONDA+UFZomIV5N3C+cCThFiAElir6sWrJhjGYB+MqxCMABnZDdFpHNHcnSypeucAag88JoeMgDNyeHw5vgqYLiZFR0wAInIbopI547k6GRL1zkDyO4G6jUDmNGLuTW3uDMGIBHZTRHp3JEcnWzpOmIAs9vWizWAubEkx7vqpk1nrYb8L7tZFeqRHTO8U9KPjw/etSMMV+1MK0Zz+4eTuYLYAJ4Z1kVTC40eNQsG4Izspoh07kiOTrZ0FQV34o+C4MofaQM4sX0oCNbt0x39jzYF/dfF2/aun92mVnfTdbUBnBhZts+qkO1l3OFYsF7VWz5RTXQJ5T2cXsptbiw0Czjfkg35b4xsA0h8osC4qagKkjrZXMH44J8kRSUG0GKm43ZlbScbPAC4YFK7X7R2xXR5lvdp82bFtHm5fOLlq5fcZlfI93pE5YLfraq+fPXlLYa1KAPQp/Ynh/WizWrkZiZc9VAUndqivCA3gNpZXmMAgztVr78mKZ4KVx2IP0kZQlp0VdmlQ4u/CgMAAEHEPfk7Jqb/fq3qtncHb5+YPnJP/DLeumJi+pGk25/eHffuSe3l39+qunmrQr7XT4KPxwW/HldWr1pjUQYwNxaf+88M/tu4w9erOCfD92dG19h9/5S+Iqg1AGUKM6kBZINJVxWKmAMQg+yLcaRzR3J0sqWrJLhk8P9YMBCVZnnXlaw5gEs2WYniTRr4Qn6x40N//M7b4x3u7cgksOrxx1c+PHpV0o2nqdyVL2QGcGZUnd7X75h08KpOmgSgpggDEIPspoh07kiOTrZ0FRmAzuxr+u7SJL9RwQCCQI3xF3KAZXtFey89NLns70Y2x5/WmbuA4s57bmyNuhBQ3Xbc04dZIi9r9Gduf91cLgZQguSjXXZTRDp3JEcnW7quGED/X4yoS4ByAzi27IHYA/YOHB/a3BkDODO65uTwVdHUym+rwaDC7Z6F4f/nhmvmgTGAEiQf7bKbItK5Izk62dK1dQiokQFcekhVtQzA2iv+sE+MrI8mL/3asgc69BzA+Mr/oCd//3RY38CTJHhUZAbw/6nhnymrRO9XNIBkDkC/xwAkIrspIp07kqOTLV01we0N3nMoemIoGIhKs7ybOnrcPzJbZrcFm4tZgNO94ve/+c74OqD/ioFOPQg2M7ghPus/M6pv6Iz78sGbo2juW6E1CTyjik6Nzn8FYO4COjWmbKJgAG+pNCkwBuCM7KaIdO5Ijk62dNUEZ1K7v1tdCJRmeVdMFp4DOD6kXCCrYKWYjyuaBPKbO2UAJ4dVZz83FprBn/3ZFEB+BWC21ZzK1xpAMn+sBoIKBlD1g2AsB+2I7KaIdO5Ijk62dFU9CLbdehCsPsu7QuWB16/Mlr3BeruClWJeDyjNbovNoEMGYO7Xj8bTDv7ZDWE4uFGdtudzAM8Mh4MfrjmTrzUA8ySw3tEqUrtWOQgk+3iiKbojOTqkc0a2dLKDE7EWkDBkH0+iDyikcwbpnJEtnezgMIB6ZB9Pog8opHMG6ZyRLZ3s4KQZwExoccvC9duB7ONJ9AGFdM4gnTOypZMdHAZQj+zjSfQBhXTOIJ0zsqWTHZw0A5CA7ONJ9AGFdM4gnTOypZMdHAZQD7eBOiO7KSKdO5Kjky2d7OB61wCsFSUqfhR4uu+X2gMG0GUkR4d0zsiWTnZwGEA9GIAzspsi0rkjOTrZ0skODgOoBwNwRnZTRDp3JEcnWzrZwWEA9WAAzshuikjnjuToZEsnO7ieMIBTW8LBTyW9fJZP3hjAqRvCcOPzGIAQZDdFpHNHcnSypZMdXC8YgFrsLQz/QPfyeT55bQB2kUWLiS7bZgAtxgUAUMbu5d+/NVjysekjtwYq+e/09Eu3rg2Ct+3RCYK/d40q0huDYMlH42KTDfjlq1e0+LWdMIC5sXDj4ejZ0KweneWTVwaQFNWtBdfir8IAAKCX2L38z9Vqz3+4Vf29fHr6p2v16s9L71WJ4YPixhVp19+hpPAtcnJY5wGbCrM0MDqfvDIAu6g6GAJyRvbFONK5Izk62dJ1Jri9waX7VMKY+O/jQSH/y+y2YOBQNGlSybxHb9ycZA/rVFL41kgSRqo5ADufvDKAGdPzMwksBdlNEenckRydbOk6ZAAmBbDq1msyQJqNakOSV3J224BKLrk56lhS+NawDMDOJ28MICuqEAzAGdlNEenckRydbOk6ZABp11+XA97eaAaD1BWCSi8fm0CPGYCdT54rAIHIbopI547k6GRLJ9AAVN3JZQ/s7VhS+JYozAHkK4QyByAQ2U0R6dyRHJ1s6bpiAIUhoFpX0Bwf+sRIx5LCt4R9q4+VT15fDYyHaw5Hz1WbERIDcEd2U0Q6dyRHJ1u6rhhAYRI43Ti7rf+6+OXjeipgdtvFQx1LCt8a5mZ/kzE+zyc/33MArcFqoM7IbopI547k6GRL1x0DSIZ7+m8v3xgzqUaCesIA1H3/4ar/aHr5LJ988iTwjjDcWPUcgOjjiabojuTokM4Z2dJ1xwCiE9vjnv7Kp+o3Buv26T30PUG9YQCaZC64A8g+nmiK7kiODumckS2d1ODUjaA9YQAnh1ceSJ7+7QyyjyexB5QC6ZxBOmdkSyc0uBP6kqAXDCB9+qvSYZ75kH08ST2gNEjnDNI5I1s6kcGdHgkCdUtQLxhANLd/OFkBtDPIPp5kHlAJSOcM0jkjWzqRwc1uC65U/+8JA+g0so8nmQdUAtI5g3TOyJZOdnAYQD3tuA20yvAq/Kyqkd0Ukc4dydHJlk52cBhAPW14EAwDEIHk6JDOGdnSyQ4OA6gHA3BGdlNEOnckRydbuvYF92p6e787GEAJGIAzspsi0rkjOTrZ0rUtuMfe9QAG0A4wAGdkN0Wkc0dydLKla1tw6uHfs9oATu3YEIbhBw/EL6dWPrwlXPnQ2Mpv71cJgefUAhBqHdA8R3yFYADOyG6KSOeO5OhkS4cBOJKuAaeyQE6tvDEMV//TmPpfGN6sHw1TK0PkOeILtJjosg0G0GJEAAAWOtXviiNbl39/a23Gd8PupV+5JwjeuscUJRniC8niVzwSLN3TWhTtNIDx8MPxSf6pUdXTT2kbmBsLVx5Q6eFXH4ieCVcftnPEYwAA4A2pAfzqNXUZ3w27l35EvV9ym50hvpAs/qK1wfKJ+b5jYdppAAlTxgDU+X5sALeojPDKDM6MqnQAeY74CmEIyBnZF+NI547k6GRL1+YhIJUG/rGajO+mOOj/vMkIYCUHKCaLXy96CCju8F948I4bQmMA6hx/bsxcB6h1oJUB2DniK/xaDMAZ2U0R6dyRHJ1s6dpsAGpx/9qM76ZYL/uvcoJZ6cFqk8VLNgA10xsmg/2pAaiuPzcAO0d8hV+MATgjuykinTuSo5MtXQcmgWszvmfFkc4KbCUIrs0VLNkAxsPBT9754ItTjQ3AzhFfIRiAM7KbItK5Izk62dJ12gBMv9/7BnBmVI/rzI01NgA7R3yFYADOyG6KSOeO5OhkS9cxA1hfU7zgEJB0AxjcabJBNjYAK0d8hWAAzshuikjnjuToZEvXPgMoJPwtZHzXxcEl++afBJZtAPHpfZhlgmlgAFaO+ArBAJyR3RSRzh3J0cmWrm3BTQbBQKOM75HyhyvSESGrqCZZvGQDMHlgbnp+ePXhhgaQ54ivEJaDdkZ2U0Q6dyRHJ1u6tgV3elNw6dMNMr5HaoTom/GGdXpKOMsQX5ssXrIBdA3ZxxNN0R3J0SGdM7Kl61Zwe9PZ4PnAAEqQfTzRFN2RHB3SOSNbOgyg15B9PNEU3ZEcHdI5I1s6DKDXkH080RTdkRwd0jkjWzoMoNeQfTzRFN2RHB3SOSNbOtnBYQD1VH4XUMXhVftxlSK7KSKdO5Kjky2d7OAwgHqm+/6mUjAAKUiODumckS2d7OC6bAAzepnnRXPqzma/wAUMwBnZTRHp3JEcnWzpOhNcE6nBXrVe95YBnByudMmHRmAAzshuikjnjuToZEsnzQBULvmMbhtAc2AAOrxqP65SZDdFpHNHcnSypZMWXOHuIAygBAzAGdlNEenckRydbOmkBddNA5gbW/nt/SqHu0r2olbwMUNAp7aEg5/SvfvUyoe3qLy/p3ZsCMPwgwfiwvHBu+LKq3YmGeBV/ee2hOFlN0eFwgrBAJyR3RSRzh3J0cmWrk3B7b306e1B/3XR7PZAL/BplgPaFASX/M9D61VisMlg2b7o8SuCoP9aXfyjTbr6tiBQS4HeNxQEv7WvCwZwo+rFbx5L+nJtACeH1bs/MAZwo8rvaLaEOhP8ePYyNYAps2VjZBcWaDHfctUG0GI4AABFdi//c7Ws5x9uNTnhj2xdPpFmfNdvdcb3R/L3ySuTS356d5AmjG+Npg1And4/G/fxB6JnVCJfZQDxxvhi4Nlk2Wfdl4+HH44vD06Nqu5+PBzcqeqsSYeAZsJVD+mrhlsKhRgAAPjC7mD5num/X6v+fjfu6pUBxJ37OybUNt3hXz49/fLVS/dMT//0al20YmL6kbjrn9699N64ILaLeL/LW42ieQO4Rad6uTvL6HJV3K3r1F9TxgDsu4KmjAFcpXv9zADGzfn+mdE1hcLqYAjIGdkX40jnjuToZEvXriEgtbq/Tuye5IPJ8sIfC9YnBTE/vv9zVwSqWL3X+cDUHMDpkf5196vVoTs+BKQ670JKr6vSW0HNHECa43HuhQfvuEEP+Jj+XpfqP3NjaaKY2DeswurAAJyR3RSRzh3J0cmWrl0GkHb9uQEcCzarkuN6DkDfFZqkfzHFSXW946TOCnPtoc7PAdTmdCw1ADVHHCYj/rUGcGY0NQCVFBgDEIXspoh07kiOTrZ0XTSA0yPBu6/9xpMj9QYQPbldO8OETAMYDwc/eeeDL06VXwFYCSAxAFnIbopI547k6GRL1zkDKAwBqQLjCKfLDCDm1Sc29d8mwACKcwDKAM6M6i1zY2UGELvDLdkHYgCykN0Ukc4dydHJlq5zBjC7LXjPoeiJIcsABg6pW0OLBtB/e3yRMPBUFJ3YJsIAkruAhm0DGNyp7/OpM4C36GcHBm+OP+Rbqj4GIAvZTRHp3JEcnWzpOmcA6ZB/YA0BBSYxvGUAKpd8tNcUDEiYA4jSu/7tIaAw21QwAG0J+7MpgHYZAMtBOyK7KSKdO5Kjky1dBw3APAj2b/JJYPX+4uvjc37LAE7rK4KvD6mSLjwIVmIA+lx/1X+0J4H3x139ZTc9P1xzo88z+jIhenZDGA6qB4nbZACijyeaojuSo0M6Z2RL1/Hg1CTwYhG0FlCTK0O3EdnHE03RHcnRIZ0zsqXrXHCnRy7Zp8761RMCi0SCAZwcXnkgfbJXBLKPJ5qiO5KjQzpnZEvXweDSkf3F7yHBANInuzqy0udikH080RTdkRwd0jkjW7oOBpeO7C8eCQaQjPjfXGkkrSD7eKIpuiM5OqRzRrZ0soOTYADSkH08iT6gkM4ZpHNGtnSyg8MA6uE2UGdkN0Wkc0dydLKlkx2cGAMw68QZzONh2YoPHUoEljHd9zuVggFIQXJ0SOeMbOlkB4cB1IMBOCO7KSKdO5Kjky1de4NL1v+Zl1cb54yXaQDmPQbQILxqP65SZDdFpHNHcnSypeu6ATz2rgcwgGbAAJyR3RSRzh3J0cmWrusGUMgCX0PnDKCQ5j1fv0Gv6nDTYW0Ad+1PKuRDQKduCMONzw8XlwSyM8Hn+eSrAwNwRnZTRDp3JEcnWzoMQFFI85735VNZbq/YADakFTIDsNLF2waQf5RVofizWqJqA2g18SYAgOKlW4NgyUcn9Mt/GQRv/97aFSYhfPL3pWuC4Df2qOLv/pquabLAJ4W3rg2Ct+3Rdb93TbDkYy2Hs2gDKKR5z/pykx34lBr9mRvT+d3H1QPBqQHYC0UXDCDNBG/nk8cAAODs5qdr9WIPK7KXFxUNwGxceu/09CNmWYjLLQNIdo5L9UZV2mo8izaAhKk6A9iZlJh88XaWgJXfsVPFFAwgzQRvV6gOhoCckX0xjnTuSI5OtnSVBrdXpXyZvS/YHGXZXwYKi0KrXABfi4tPjyzbF0XH0yzwJhVMcKXeWe2hUsVMdjQfQGmadzWac9lNL0b5pK/K95UawIzp2E/WzQGkL+1sktWBATgjuykinTuSo5MtXZXBJSP+s9sGoiz/o20Ap0esGYEf3/+5KwLLAFRqSL2zyg+j5gXiDR3LCdwgzfucSe6y6juZAUwVDCDv3zGALLxqP65SZDdFpHNHcnSypavWAMzQTaASAOtV/1WXnxuAlQogqWoZQFo4aeWH6ZgBNEjzHvPCHRtCPZyfXAHEhVwBzBtetR9XKbKbItK5Izk62dK1xQCWPbCAAZweCd597TeeHBFiAMU073q03xq4PxmXLjgHkO+UGwBzAMKQ3RSRzh3J0cmWrloDsM7wrSGgZEDHGgI6FmyOzLbyIaCOG4CV5n1K3fb53LCexF2l7vt/NtQGEFc5NWbfBRT3+muSmtZOlgHYtwlVBwbgjOymiHTuSI5OtnRVBje7rf+6uOd+XHX+e7NJ4PilygIc6Eng66N444AyhkMqJbAygP7b6yeBOz8ElKd5t+/uz27pjzv8G9NU72XPAZQ/EmDlk68ODMAZ2U0R6dyRHJ1s6SoNLhkDUkkfzct3Ky84pl5dsinu1M3GuPj0SDpWFE0GwYDu8fOdO28Adpr36GT++K56Ejj80AF9F9C398cv1ZCO9STwjjDcaGrmO9lzCHk++epgOWhnZDdFpHNHcnSypas2uBPb4y583b705ZU/0iNBjw0F657WnXp80h+ozMD6xcXXq7P/0/F1QFIY79F/5VNRFwygrVScT1728URTdEdydEjnjGzpZAfX2wbQnnzyso8n0QcU0jmDdM7Ilk52cL1tAO3JJy/7eBJ9QCGdM0jnjGzpZAfX2wbQnnzyso8n0QcU0jmDdM7Ilk52cD1uAG1B9vEk+oBCOmeQzhnZ0skODgOoh7uAnJHdFJHOHcnRyZZOdnAYQD3Tfb9UKRiAFCRHh3TOyJZOdnA9awCn7mzbR2MAzshuikjnjuToZEvXkeDmSQz26nz7yTYAlSc4WUWolnbmCcYAnJHdFJHOHcnRyZauywagUsI3BgMoAQNwRnZTRDp3JEcnW7ouG8B8GYGlG8A8YAAikd0Ukc4dydHJlg4DcObUDrVS0AfVgqFTKx/eEq78v9OsMlNJbpnouS1teA4AA3BFdlNEOnckRydbunYHd+KPsiWBTmwfyhYK2hQE/2KfWiM0UOuFZiWz2wYmg2X7Zu+L3//WPtkGkC71qdZ+m1Jrha5+ttYApsz7jTV7tpjpuGoDaDXzMgBAKXlueCvpe5Yc3qSEL6SDv2htsHxit36/5LZWv72tBjAefvhwfBkwanp6vQSoQmcGMENAM+Gqh8rWAmrxV2EAANALxD38Oyam/35t3MtP7w7ePjF95J74Zbz18ukjfxV8PN6o/KCmZPrlq5dPTE9/V71sjbYaQNbhX5X80czolAHGAMzC0NGZ0YqXg67YAKoMjovxFpAcHdI5I1u69gZnJQazMn5ZyeHVHEBJOvj+dfc/FUmfA4jmXnjwjhuSsZ7kJP/ksJVSfixNM1N6p5ArGIAzspsi0rkjOTrZ0rXbALLUwFbOXyt1pDKAumzAKklMEPRfe0i0AcztSIf8cwM4M2peaAOI3ySYhPIVgQE4I7spIp07kqOTLZ1IA4ieVIllAtkJYcbDwU/e+eCLU5YBzI0lSz8nVwCVdvwpGIAzspsi0rkjOTrZ0kkbAjIGEPPqE5v6bxNsAMkjYHGfnxvAVDrYk8wBVJsJJgEDcEZ2U0Q6dyRHJ1u6NgdXyA2fJ31Pk8PrlPB16eBj23gqik5sE24AgztN0t/MAGaysZ6Tw285rN4P3hxbxLeqTgqPATgiuykinTuSo5MtXZuDs3LD12WMV69USvi6dPCxI2gGRM8BjKcj/GsSA8gnfdeoZwRiX9jfhikAloN2R3ZTRDp3JEcnW7q2Pwi23XoQLEn6biWHVynhD9Wmg49mvz6kcsbLvgvIpPu66fnh1YfrDCB6Zlif9j+7IQwHN1Z5D5D044mm6I7k6JDOGdnSyQ5OsgF0C9nHk+gDCumcQTpnZEsnOzgMoB7Zx5PoAwrpnEE6Z2RLJzs4DKAe2ceT6AMK6ZxBOmdkSyc7OAygHtnHk+gDCumcQTpnZEsnOzgMoB7Zx5PoAwrpnEE6Z2RLJzs4DKCeam8DrT68yj+xOmQ3RaRzR3J0sqWTHZw0A1h4dYd25gIzTPf9TXVgAIKQHB3SOSNbOtnByTSAeZIBYwCSDyjZTRHp3JEcnWzpqgvu9KYg2DxPubXOz2LBAErAAJyR3RSRzh3J0cmWrrrg9prFHRpy9hjAfGAAlX9idchuikjnjuToZEtXWXAml0u1dNwArDzv43lmF7Oiw02HUwMo1LprRxiu2ql3viEMNz5vDCDPBm/yxR+o7ldgAM7IbopI547k6GRLV6EBNH2CvyCdNgA7z7tlAFNZXi9tAIVa+Uuz+Q+0AVjZ4E2++MJwUYuJLis1gFazbgIATP8k0Pndp1+6dW0QvG2PSge84pFg6Z54QxAs+eiE2qAy/b50TRD86v+49nL19nvXBEs+pna26uidjtwTf8hb97QcVLMGYOd5zw3gzKh6eWos/qsNoFArHNxp8sDEfzYejp7Vi8DZ2eCtfPEYAACclSQG8NO1eiHnpffGfflFa4PlE8mGFYkBJG9VUvit6atpu47eabd+v+S2VoNq1gASpuoMYGdSYs8BmFo6G/xM3OufHNan+VPKAOxs8Hm++IpgCMgZ2RfjSOeO5OhkS1fxEFAh64vK9KgzwsQbNusK8UaTIGa9Kh84FE2aVDF5HbWTSRH2eLC+85PAeZ53awhIjfNcdtOLUWYAZbVmTNoX9bKQDX6q6rRgGIAzspsi0rkjOTrZ0lVrAIXsjmpSOMkJObvNZPvKUkSuTyaN1Q6FOmZj/7r7Vd6AThuAnefdMoA5k9hl1XeMAZTXmjFn+uaSwcoGjwHIQXZTRDp3JEcnW7pqDaAuv3uS7CsI1Nn/pYeOmScFVDVTblyhUCfSecKCoP/aQ502ADvPu30XUBS9cMeGUA/06zmAslqFKwDrZlEMQA6ymyLSuSM5OtnSdcoATML3+Q0gTwr/5HZtCBOdNYBCnneT0X0qz+d7Mi5VXXuxVmYAhTkAq9PHAOQguykinTuSo5MtXRuHgIwBrLcrFIaAcgMo1ElevvrEpk4nha/J8x536M8N6+ndVeo2/mfD1ACsWoWZgjXJDoVs8BiAHGQ3RaRzR3J0sqVr4ySw6eD7r1MTukMD9ZPAqQHU1NETB09F0YltnTaAQp5367b+/Gb/ZAgor2UZgP0cgJUNHgOQg+ymiHTuSI5OtnQVG0AynNN/e3oyX7MhHe6xDaB+p72m0kDHJ4GtPO/RyS3h4KfyJ4HDDx1I7gKya9kzBad2hOHGZNIgzwbfBgNgOWhHZDdFpHNHcnSypav6SeAT2+Oe/Mqn8tEctSFYty/dcGJTEFzybwpzAHV1otmvx5Zw8fXy1gKSgOzjiabojuTokM4Z2dJ1JThr1H9eMIASZB9PNEV3JEeHdM7Ilq6zwZ0euWSfugqYd9nQHAygBNnHE03RHcnRIZ0zsqXrcHDp6P7iamMAJcg+nmiK7kiODumckS1dh4NLR/cXBwYAAABVgAEAAHgKBgAA4CkYAACAp2AAAACeggEAAHgKBgAA4CkYAACAp2AAAACeggEAAHgKBgAA4CkYAACAp2AAAACeggEAAHgKBgAA4ClngQHM7Rg22YlhcZwZXZO8KkiHjgvw3A1hOFiqFtItwHNbYuluOmzeIF2znNQZ2aN2SNf7BnBmNFSs/E63A+kZxsPEAArSoeMC7Nf6hIN3qzdI1wxTRrpV9Woh3SKYGwuNAbRBut43gPFw9YHoVCoRLMTceJgaQEE6dJyfmXDw5kgJpFsc0jXByWEt3RZz3CFds8T+afRpg3Q9bwAnh3WDPDNqzsxgAdTFeGIABenQcX7is7Bb1P/j865bkK45xsOr1P+MTkjXLCeHEwNoh3Q9bwBTSW82ZQ4ymJ/4ZGLjs5lklnToOD9nRpNr7fE6tZBucRgJka5J4lOPPzVzAO2QrucNYNycmMVX6GsWqAkxU6t2ZlIVpEPHRaINAOkcMFOZSNfyEnlaAAAgAElEQVQk4+GaZBK4HdL1ugHMjSVXQNlEOSxEcsgUpEPHRaIvuZHOgWeHVZeFdE0yE64+bORpi3QYgH9gAC2gr7yRrmnGw3BwZ4R0zaJPODCAhlg6cCvZIqk3gJXfQcfFMaNvA0W6Zpn7X/6b4XDwU0jXLHrEsc4AqpPuLDIAziEWCVcAzswMD6qRV6Rz4Tk1BoR0TTGl7//hCqAhHELNgwG4MpU8BoZ0TqjhbKRrhpPDWiAMoDHcR9A03AXkyP4wveca6VzQfRXSNUHyCHXyxC93AZWQ3gbLncSLZqbmDuLktmJ0nJ+58WQtgwjpmiJ9hs4YANI1QdEA2iFdzxsAzxI2TWoAPJPZFOPWQ/dI1wzj2SXnGqRzIRnm4UngEuLTi1WsJtIUqQEUpEPHBZiylUG6Zjg5HG48HM3tT+6gQrpmSQygHdL1vAFEp1hPsEmyUcOCdOg4L8nai2GykhLSNcNMspCqHglCuqZJJ3rbIF3vG0B0akcsw4c4g1g0+bRRQTp0nI+ZfDRWq4d0zXDKTqWAdM2S3elTvXRngQEAAIALGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAB4ysE+m/O6HQ5AF8AAwFMwAAAMADwFAwDAAMBTDva97qvdjgGgu2AA4CkYAAAGAJ6CAQBgAOApdQbw2qf7PvDD9/X1vf7P4jevfOb8vr5z3vzltFAVnPP+R81Ov3hv34U1H1Kor8pf+9Ib4496/6PJ/rr8lz+rXv7s/GTvuBpTD9BVMADwlDID+B01H6w2ZzPEF6RFmjd8sYEBFOvH5f/sfeb9uV+N7PILTOkbtC/ETvCBzvxWgHIwAPAU+y4g3YurXv6cP4te+awuUyfzP/9C0qPviv//aHwWn1StM4Ca+nF53zm/F+8Qf+KFyXe94cvRa18wb3dlrsEgFHQXDAA8pdQAzBl5fGp+XloptoR80GZXuQHU1lcGoD8p/kh1sp+e88dvVfWjpjR+xwgQdBcMADylzABelw7YpKfmppNOT9lVT15mALX1s0GeZM+jqbMcTf1B9fyMAEHXwQDAU8rmAEy3bZ+a74q3We93lRlAbX1rdrfWIOxPYQQIug4GAJ5SZgCm21YjOPbVgXW3TuldQLX1664QdqUXBAn6ioARIOg+GAB4SlMGcGFhp1YNQJczAgTdBwMAT5nXAArn5ou4Aiieyy9kAHrDUUaAoOtgAOApjQ0gmwyoLaifA9iVzAGUnOFbX5JNIqefpCaDdzECBF0HAwBPaWwAcYetbtZJtsV9+0GrA08M4Dz7fW39WgM4at1fql/EFX7lvYwAQdfBAMBT5jGAuJ9OTulN153d53/Q3DGanfIf7UufAyjUrzWA7LbQ7FJgV9/rz2cECLoOBgCeMo8BqI7+3M/GW77YZ3pu86SvepI3e/JXPdn7pfPz93b9uieFjxaeBDYbGAGC7oMBgKfMZwD5U2LJmX3y/pz/On9o2FobqLZ+47WCLkg+P3tWGKCbYADgKfMaQPTKZ97Yly7fad7rxT7Tncxan79vrwZq1W+0Wmj+cflgEEAXwQAAFk9lT+8eZAQIBIABACyeqgxA5R6o4nMAWgIDAFg8VRnAz7gHCCSAAQAsnooM4JVPZ4tLAHQRDABg8VRiAAfTu0kBugwGALB4KjGAo31953554WoAbQcDAADwFAwAAMBTMAAAAE/BAAAAPAUDAADwFAwAAMBTMAAAAE/BAAAAPAUDAADwFAwAAMBTMAAAAE/BAAAAPAUDAADwFAwAAMBTMAAAAE/BAAAAPAUDAADwFAwAAMBTMAAAAE/BAAAAPAUDAADwFAwAAMBTMAAAAE/BAAAAPAUDAADwFAwAAMBTMAAAAE/BAAAAPAUDAADwFAwAAMBTMAAAAE/BAAAAPAUDAADwFAwAAMBTMAAAAE/BAAAAPAUDAADwFAwAAMBTMAAAAE/BAAAAPAUDAADwFAwAAMBTMAAAAE/BAAAAPAUDgDL6mqHbwTrS1G/s9o/sqWChZ+BQgTL6fmnx9Oox1Pc7TdDtH9n3N03Q7WChZ+BQgTIwAAwAPIBDBcrAADAA8AAOFSgDA8AAwAM4VKAMDAADAA/gUIEyMAAMADyAQwXKwAAwAPAADhUoAwPAAMADOFSgDAwAAwAP4FCBMjAADAA8gEMFysAAMADwAA4VKAMDwADAAzhUoAwMAAMAD+BQgTIwAAwAPIBDBcpovwEc7Luw4pib/YrKDeBo33mLjOy1L12wcHzFYNtuAIuO5hfvfd1Xk1+wqKogGAwAysAA2moApmYvG8DifisGIB0MAMpgCKgDBtBUsHKGgDCAs4hebbzQXjAADKARGMBZRK82XmgvrRnAK7/d19f3pt9P3nwmfvPmL5s3P3xfX9/rdUEy4lAofeUz5/f1/XJd1UKtXX0f+MEbs12KBT97X985vxdF/+n8vtd/Nv8K+4MKv7E1AygE+4Pz+875fd0p7jJfetB0kPZ3/1Cp8vr3Pxq99mmVtvG8EgmKP64QbEsG4PoPkpdaPyvu1f/P5BfYP/Zo34WxCuf+mfVDMQDxYABQRksG8LPzTWZa3QUeNW/O+YB6c9AUXBil/U2h9GcNqhZq7ep7k36je5Ziwe/qdxfu0pv+LP0K+4OKv7ElAygEq7+y73fqDMD+7i/0paoUDaDhjysG24oBuP6DWKWLMYD/It73DY9aPxQDEA8GAGW0YgBx//Yr8dnfD89XnXDc97z5H6PXvtCXvIlLfqD7N93fFEp/8d6+C+LT4y/qYrtqoVbc1573qDrj/kB9wQWPxnvHp+LRK59W3U/6FdkH1fzGVgygEOxR9Z2v7eqrNQD7u+M66qrkBzpWaxK44Y+rCbYFA3D9B7FLiwaQDgEVDUD1/sUfigFIBwOAMloxgF+8V/UeCclQSNxVnJe92aX6N93flJUWSpKqVq1dppvZVbe7KYg7LX0iq97UfVDNb2zFAArBmn7wtU/XGoD93cn2uNIHCgbQ8MfVBNuCAbT4D2KHP68BmG+xfygGIB0MAMpo8Qrg3L/OX5uOV/XI2RuF6m9qSpNu6mfnF6sWapmuyfQ5tQWq40k6u8wACt9Z8xtbMIBCsGkHe7DGAOq+++f/73//vr6iATT8cbXBtnYF4PYPYpUuxgC0eRV+KAYgHQwAymhpDkAPHZ/7r1V3kJ18qq6g5kz0wmKpGRlPhsDtqoVaxZ6ovqDWAArfWfMbWzKAQrCmm6udBC5+9yvvS3YoGEDDH1cbbAsG4PgPUihtwgCsH4oBSAcDgDJauwtI3cnSp4bk8x7A9ChWd2D6m0JpSZ+aFuYvansiq+ADUZkBNOyCWjGAQrA/O7+RAVjfrSZiz/nl3/3rT9caQIMfVxtsKwbg+g9ilS7GAM6r+6EYgHQwACij1ecAXvv3v61vH3E94XS6Aig3gLZcAdQEu/AVgJogeDSqnwPoyBVA1Po/yKINoPBDMQDpYABQRgUPgr32RXscuckh5/nmAOrH2JOCMgNo2xyA9bGLmQNI68SXDvPOAbTLAKIW/0EWbQCFH4oBSAcDgDJaMYB0RETNj5beVnIwu0ezptQMIiedt121cKNM3V02aUGZARQ+qOY3tmAAxWDtu4DMF9mvC1cDR/Vt9Y3vAmqHAbj/g9T+G+ifZRmA/WNrDOBo3/wDcCACDADKaPEuoDd8OUruxTc3lv/8M9aN5T88P7vrsFAanzOq/bJb/LOqhVo199nbBaUGYH9QzW9sxQBqg71Q3Tyf3A6vn0PInwPQ321GRtRjCsYAlH3US9AeA3D9B7FLrZ+VGID6BfaPLQwBJT8UA5AOBgBlVPIk8Lm1D+umj5bmS2EWSpM3fRfUVK15WNbqI4sFpQZQ+KDib2zFAMqC1U8Cm/uDXve/9p1X/O6j5uUbvpj0wnGAZU8Ct2UIyPUfxCq1fpbu1c0vsH9sOgls/1AMQDoYAJTR4lpAapkcvRhM5LAW0Gdrq9Ysl2P3kcV1dKIyA2jvWkBJsD94Y7oWUPRaHNK5X066Q+u7k5fm1Pk/nZ8ZQOMfVwi2FQNw/gexws9/lunV9S+wf2y2PJz1Q/9/9t49yo6rvvds85hxDBm4cbklR1hX09WyjJRAQA3OvZYRZCYv2kMQti8QWlmxsVp2iGPDnbmsOKxribUS68qJdY2QydyZUYhnuA2YFQW1mFxjwMNDujAJso2x1X5GwVZLjSNZPUjurv+m9t712FXn0efsU6fP75z9+azldp16nPM7X1Xt76ldVfuLAUgHA4B6MBrokgawzMV2ZAAA9WFXgXpgABgAeAC7CtQDA8AAwAPYVaAeGAAGAB7ArgL1wAAwAPAAdhWoBwaAAYAHsKtAPTAADAA8gF0F6oEBYADgAewqUA8MAAMAD2BXgXpgABgAeAC7CtQDA8AAwAPYVaAeGAAGAB7ArgL1wAAwAPAAdhWox1A79LpYR9r6jr3+kn1VLPQN7CoAAJ6CAQAAeAoGAADgKRgAAICnYAAAAJ6CAQAAeAoGAADgKRgAAICnYAAAAJ6CAQAAeAoGAADgKRgAAICnYAAAAJ6CAQAAeAoGAADgKRgAAICnYAAAAJ6CAQAAeAoGAADgKRgAAICnYAAAAJ6CAQAAeAoGAADgKRgAAICnYAAAAJ6CAQAAeAoGAADgKRgAAICnYAAAAJ6CAQAAeAoGAADgKRgAAICnYAAAAJ6CAQAAeAoGAADgKRgAAICnYAAAAJ6CAQAAeAoGAADgKRgAAICnYAAAAJ6CAQAAeAoGAADgKRgAAICnYAAAAJ4i2gB+etHQhdmLI0NDQxdbSy6uu0kr/ODXLhoaevVv/sNS6+0dSnn9Gz7v/GkN3vq1P1zetQAAyog2gFc+PvSqL+TTQ7kdxG7wXtc3/WzSql+w1DvkBhDzhkobWQwAAHqPaAOI2/kL/jiZjH/zD2V2YDtDu+TNevbeS66pqLSVxQAAoPfINoCT+Q/9+Df/b1yUvvrZu62+ofaIjeSC344bzB+8Z2ip99ibuswrf/+JoSXX7gI07QDQTWQbQN7Qq9/8/+e7047/k+49QNlJRfzeS5xF7LVWOLL0CUP1YAAA0E1kG0Dc7CdNoL4enDWIezvpAUob1SNLNemFT9nbg1MADAAAuolsA8gbaX3V92TyKveFv/+EujTw+t9Ur/Iem2TxS2rhBQ1v4DnZlgGYE4bs7iN9bhL/ufiVv3yduqUobajtglQhF+q+pgveqDqdfk1dS/588tbJF9A1/vwn029bPLEprpV/E/0y28qsVacW9YHxJx9xd0sAGGiEG8BPk25/c9U3fZU2w9kNPUOv+YLVL5QsPpIufGP9917yNKKwQlxB7BdxM2vaZF1K/OpfvCcvoFyQ2ua/Ta4kv+oLn7WvPKdN+5FCiY0MIF3rgt/Ss79f+JDcAEq1fDz55E9gAABQF+EGkF4EME8E6F/UUX5eEDeM6qf1S+Z67s/SSwRm8RHzc/ufP9vAAQoPGdSl6BCmdU7n6Z/V8SfGjfIPo5c+njyiUCzItMHxDOULrx56wz9Er3wuuZsoadrj9V/7eb344vwj7ALStV7zV/HJxXvM4rjweCP9Xhfma9XUsjf7aAwAAOoi3ACsNvC9UdLsZl082SXi5Hd5sq5ZnLfv9fv6f/aeJdvFegaQnGYYK1KNbnqCkjTChYJeSRtjteKF9ntmjXZacd1aimulq2VdOuX3KtSSdVYdwQAAoD7SDeBk2uTrRsx026c/9bNO/GRx8tp0FB2xHxqo/amv2ualHwQrGcDFWRtvPiNrmpNVywXlDfve/FqGnjCNdtZp1eByhFkrNzDTqJcNrWQTSS3Z91/6bicA8BTpBmBa2vTnvGn6a28CNW1evDQ7TbBb/To306hOmiXHkqh3BlBoXvO7VMsXWvcWTlWstzppG8BSl2f3Jic26Vrm806q68F/VV6rVIv1/Tu4ZQoABhrpBhA3ZBdbneN7VbNWaNH++e//93/7uqG0M+TCtCVUPSI55RZQPVa89FhCxZZzr6lBu0/SvGZXHeym3Coob4XrG8BSt3nq5bmLpJPmcnI2mFF+F5BVS/5ySZsBAF+RbgC6ecu7UlRjZj0G/P3XFZp43V+UdRM1NgD1VNdvFz8mXd/uXalzF1DS0Ca3I9UaQLGgqgygfC6jbyhVt/t8Mp+JAQBAu4g3ANWo5xd0f3rRBX+c3gya3Oj4+l//d/+QtK8/e3fcupZ7RGpQN8a8pvxwwJIGoO69+aGZ+9ofnkw/r2gA5YK6dAag+Pt/q63mvVFjA0i/PwYAAPURbwCqUc9vj1TN3JH8dEDfdBnl7eveoYuThtBqK8vsbXVgt/pPAqsmfK99ScKUYq4MFAta2gDyR9fq2VX9awAp6T2ldQ2AawAAsCTiDUBdBLDukoyb3qT1tdr47EaXk0Ov/X72qFX6Y77kBUcaPhlWxm45v5+dHMQf9gvmanOdRrdY0FIGYD+6Vu+WpPRScbrwpLpyUdO01zUA7gICgCURbwBxS/bfWD98Tw69Or3BPW9vs1vdf/buV/0PybpZl03ppqGln//KyH+g6073vNl99UXpxzUwgCOtXQPIbt1s8CO90XMA+c2mDc8AeA4AAJZEvgGcTPu6Ner+nfSnfdLjop6QTeftzdc9Yi6SZg/fJhQG+W/+JECDPICTmRk06ALKClrKANRbtfck8MXmU/WA1i99vMk1gLQYngQGgIbINwB1eTZvwdR11rQp/lky9s3QGz6XP1GVr3ukpumOyncHtWEAb8zeJHvmtqbRLRe0pAG4jQWks3E0F+dr1RjAK0n5r/mfMQAAqIt8AygNxHzEuoNfj36pHoqyx2DI7+9/6RPqTpl00ExD3ni2YwCvLwQI77W610uNbrGgpQ2grdFA35gW8cpfvj4u6tXJ6wYGwGigALAEfWAA4jjSg3CwDiBVAADqgwG0Tfy73jWObBlZ6hZTAAAMoG1+elE/dKkcsW4xXXrUCwDwEQygXV76eF80qHG7r4a7eOX7F3EJAADqgwG0x5G+uavyZHq5u2bYCwAADQbQHif7p0H958+am6C4AgwA9cEAAAA8BQMAAPAUDAAAwFMwAAAAT8EAAAA8BQMAAPAUDAAAwFMwAAAAT8EAAAA8BQMAAPAUDAAAwFMwAAAAT8EAAAA8BQMAAPCUwTWA2dleV9AYuaVJVg3ZnJBbGqq5UK1qGEAvkFuaZNWQzQm5paGaCxhAa7B3uSBZNWRzQm5pqOYCBtAa7F0uSFYN2ZyQWxqquYABtAZ7lwuSVUM2J+SWhmouYACtMTsEYNPNnU1ue+FNU1YtckvDAFpjVvBX82XvqpjOasMApIFqLvSVASzuWvutaC68Np6cvyf+oyenRz/T3U/VYAAuSD4mMQAn5Jbmr2oLO1YfU/9F0cvmRXFR88r60wDOjq+PMIAEuTu+5GMSA3BCbmn+qpYZwCPvPDj4BqAxBqDBAOTu+JKPSQzACbml+ata1spPrTzY7rYYQGtgAC5IPiYxACfkluavaoNnAHFL/81DYfi+h/Srp68Jw8tvi6wuoJkw5lqrC+jsuLaBZ7Yna0Yza7+xPVz7UEX1YABuSD4mMQAn5JbmhWpTK7/6xSD4VwfU9JmdY0Gw6UDWBfTkjiAIRowbnNkaBP/6QOoNj14ZBMM3HFOvfrQ1GL4x3njhi2P6fcQawJ+rNl616ou7Qs0VxxsbQNL+z5g1t6jJtbfoTSxmO2Kos81hwGB/gB6wb8UH41Y+uOTO2dmfbFBTwYr7Z0/csepw/N/37ohfrlEvkmXJotmH9YrBxvhVOjW7L0jfpyO6ZgBheFu0OB2uV6366J74t/143Ng3ugg8P6nb/7lw3dejaH57eLveqtwz1Nk35YAHG/YH6AH7gkv+/eyJ+4I1avJth81kYgCHY3tI3UC19v8p+Ih+9eJ1K/bHfnGdnr9G+cGa2RfjV7Oz31ZW0BHdMwB1r+f5ybXfWtxlWvKz41ccb2AA/9G0/+nVgPOT2jauragWA11ALkg+K6cLyAm5pXmh2tTwXZHq2Fl58NyE7vhXk9ldQOoagJo4NzGSrJ9cHvjxA5++MlArqYsEastzE8ObHngiknsNIGvSz0+ajhzV+Nc3gDDp60n7ivTLGXUaUCEYgAuSj0kMwAm5pXmhWnKZ9+jwXafHNkfJZI0BpMsSAzg9pvt70ocF9N+jas7wDcdkG0DcjOcGMPqZBgYwuj05X0gNIF4LA5CA5GMSA3BCbmleqOZiAOcmgqtu+MrjEwUDiB7fqU3hsGgDaOkMYPQz5hpwfpNoFGEAIpB8TGIATsgtzQvVXLqATgXbItPxYxtAzMuPbR2+U6gBtHMN4DNxc69eTluNPgYgAcnHJAbghNzSvFBtKrjsQLTwxWBETV59zEzmBhDbg3kR3BRFj42NJAYwckzdF2obwOmxkSei6MwOuQYwukf9bXwX0JuPWwZwflK193Ph6G3xtl8zW2EAvUfyMYkBOCG3NC9Umxq+UvXcqI6gpGc/a/NNx/6I1eufLDo3YW7+NKcKyRnAlJk3IvYawDVJZ37d5wBiAyg+CDanVz2UXQLoggEA2FS6e5V2Nh+assrxQrWplV/dGQSbdBfOmXhq+OonrLGAzsU/85/MHgS7LH0QTL14001T5mJBMm/hy2Nqpty7gO6NW/P3mwe5ap8EjqKnxuPf+dZgcNN6rlpzdIvaqnID8GHvqhzJqiGbE3JL80I1h8EemiPWAJZjgJ828GLvqhzJqiGbE3JL80I1DKBHeLF3VY5k1ZDNCbmleaEaBtAjvNi7KkeyasjmhNzSvFANA+gRXuxdlSNZNWRzQm5pqOaCTAOQB3cBpbSlmtwd35+DslrkloZqLmAArTE79HOgwAC6j2TZ5JaGai70wABajfCyUr+qeLvOwAASMIDuI1k2uaV5rtq5rUGwTaXCtwcG0BoYQAIG0H0kyya3NM9Vm1JP/qpU+PbAAFoDA0jAALqPZNnklua3amao//ZvEsIAWgMDSMAAuo9k2eSW5rdqZpSHfjGAp8bDdXvU9PxuNeaPDn+fHr13d5jOvjkMtzw7vj65HdQM9mkMoSb3fblC4Xvd8goBA+g+kmWTW5oXquVh7yNHg5UHsrj3U2pkt/9ep8Jnee+xH6x+cqfKgF/YGaihQ5Phg24w4wGZePieGMAfpIHvaky3dHo6nzSzfydu8PWQPou7dOTXnMn6Lee+L1cofK9bXiGQhQvQK6yw90s3BKsOZ3Hv/6T+9y91KryV975v1Z+oyd+9I0mCT3Lk18za8fAd4WQAyVDPOs3rA3EbPT+ZJHslI0DHf7Ycj55Ww73pX/2xH6gBPlVLXyf3fblC4Xvd8goBAwDoEXbYu2q787h3nf4+q1Phrbz3fcGq/bPf2aD+fjtQofHB23WOvN4+iYfvDDcDuFa31nkP/4wxgHS2yn6JTMfP4q54cm70L+I2XUeD1cl9X65Q+F63vEKgC6j7SJZNbmkeqGYnfSW9/Uncu3UNIMt7T/LDzKpJCozZdiTK4uF7EAlp2unkEu/icw/efXOYD+usZifeoNdQc6fXfmPSDP5fL/d9uULhe93yCgED6D6SZZNbmgeqlcLeozzu3b4InOa9Jy/tGDDT72Nng/XWABZ3J012yQD0r3e9Rjy9uGu9OhFIkr9qct+XKxS+1y2vEDCA7iNZNrmleaBa2QDyuPfCXUBJ3vuxhgZgZYP11gDixvtj9zz4/EzjM4Dzk+vPjl8bzaz95q40EiwlNYBlCoXvdcsrBAyg+0iWTW5pHqhWDHuPojzuvXwbqMp7v6vWAFL/iGQYgO7WTyPg89nWNYB49tq/0xd//3Bc/cqvk/u+XKHwvW55hYABdB/JssktzQPVimHvygDSuPfUAOJWP8t7rzGAhR3DN8aTj2bb994AVN77/PZSF1Dckq8/rmLgVas+N3pN/MP+/GSY3OhTk/u+XKHwvW55hYABdB/JssktzQfVCmHvugso6dJJZqhU+CzvveYaQClHvvcGkN77H4brC7Pz5wD09Hp9kmD6d2pz3wmFX17aUk3wMUlT5oTc0rxQrRD2HuVx76kjqLOBNO+91gD0g2DBpgNSuoCixUNxU3/5rc+OZ3d4mpuD5neH4RYzafqH1EmB2bom951QeLmlSVYN2ZyQWxqquUAeQGuwd7kgWTVkc0JuaajmAgbQGuxdLkhWDdmckFsaqrmAAbQGe5cLklVDNifkloZqLmAArcHe5YJk1ZDNCbmloZoLGEBrsHe5IFk1ZHNCbmmo5gIG0BqDehtol1WTu+P7c1BWi9zSUM2FvjGAwkAPiuVJAksZ0AfBMACJSJZNbmmo5gIG0BoYgJNqcnd8fw7KapFbmm+qvbzkGsnTYk3BAFoDA3BSTfAxSVPmhNzSPFPtkXcuGf+LAVQHBuCkmuBjkqbMCbmleaZaC/nvg2gAeey7lS2fJcATCt8mGIBEJMsmtzTPVPPTAKzYdytbPk2AJxS+XUj4BehDTpj899kXPhUEl3zosJr17V9KJ1+4Pgjeul+ttOp7dwSXfFgtzVY8cceah4MV+0/ctyEI3rK/0zqW2QDs2HcrW34mHSuaUPg2wQAA+pDEAH6yQY/vrKLdHzaDQG+cTWauuD9e6RevL8xTK56449INwarD+/TrS+7ssI5lNgA79t3Kls/i4QmFbxO6gCQiWTa5pXmmmu4CmgredSxa+GKwLTo3sfJAFJ2eULEvweZo4UvxvHhq9YHoEZ0MkK2oF5sUsejRYHNfdQEVYt+t4aPN2M+EwrcPBiARybLJLc0z1ZQBnB7TEZELO/T/fvzAp68MVh+zcyOH7zJtvbXiwg7lHOcmhjc98ETUZ9cACrHvNQZAKHz7YAASkSyb3NI8U80YQJLynqd92cm/deLg1QmCvjJ8VEeD3XCsrwygcAOnmQEAACAASURBVCtonTMAQuHbBQOQiGTZ5JbmmWoFA1h58NxEcNUNX3l8YgkDWHkwvTXo8Z3aEHqQCOaGuQZgNes1BkAofPtgABKRLJvc0jxTzRhA2tZHp4JtkenusbuAcgPIVrTuDX35sa3Dd/aVAdix77UGQCh822AAEpEsm9zSPFPNBAEP36iu5Y6NxAYwckxFBOuLwDdF0WNjI5YBWCuamafHRp6IojM7+swA7Nj3WgPobih8r9vqroABSESybHJL80y1o0EwkvYBxV5wbiLt40lmZkHx+m++YnIGMGVWH+mrawCRHftexwC6GQrf63Gbu0SlItWqJviYpClzQm5pnql2Tv3aj86onvxNB+LX8a//4E03qfMCPXnZAbsLKMpXTAxg4ctjav3+uQuox3i2d1WEZNWQzQm5paGaCxhAa7B3uSBZNWRzQm5pqOYCBtAa7F0uSFYN2ZyQWxqquYABtAZ7lwuSVUM2J+SWhmouYACtwd7lgmTVkM0JuaWhmgsYQGv07V1AvVVN7o7vz0FZLXJLQzUXMIDW6NfnADCAhsitTbJscktDNRf6wgAWd9WPf5zTYz7P39OdTy2AATipJnfH9+egrBa5pXmt2tIJ8SXSESH63wD0o2BdBwNwUk3wMUlT5oTc0nxWrYWE+BIDYQAaDKAZGEBD5NYmWTa5pfmsWgsBwSUwgPbAAJxUE3xM0pQ5Ibc0n1UbLAOY331NGIbveyhS2e7f2B6u/fqu0XsPJXOi6JntYXj5bWpKdQHpIPhrC7OnR+/dHYbr9lRSTAIG4KSa4GOSpswJuaX5q9rCDjWsmxrvZywZGChKW/hTKv9RT2YLF3aMHA1W/o2eN7FyvzwDODtuBvVUv/pn1t4Shlf8467Ra9I5psUPwy1R0QCs2dP59jadpR/3qwF0GPoMAMIpJsSvuN/MfVglvu9TOfAvXrfRWmhS4b93x6rDL14nMhR+OvzA8fg0YNI066oZX9wVju6JFqfVQP9z4bqvx0u3q9E+rYvA9uxpvfausNQ11Nk3xQAAQCb7VKu/L3jb4dkT96kmX/FPwUfixv6X4yVqKl8Y28VGZRqxB3Tc/nfFABJmjAGo3p24MTehv1kUcDy9vmAA9uxpvdFc2QA6om8NoEINHFQTfFZOZ4YTckvzWTV1DUClgUVREvoeqXQwlRfw+++4Sy21FpoVFnZctlUNHS3xGkC0+NyDd9+cdOzYab/T4e2xFyRccdwygMJsKymgMjAAJ9UEH5M0ZU7ILc1n1ex8yKOqXdczVx87uvJvJ7YZJ8gWpikBQaCuDwg0gMXdSVteYwDxq/OTaUuvEyJTAyjMxgByMICGyK1NsmxyS/NZtboGcGrlwdgDpkZOj22rZwDDfzYRrynQAKbD0Y/d8+DzM3XOAEY/k05q7DMAazYGkIMBNERubZJlk1uaz6rV6wKKG/2PTmyOjq7+UnFhnhN2KhgRaADnJ69QmY6Lu2wDsK4BWFmP9jUAazYGkIMBNERubZJlk1uaz6rpJMip4Opj0cIX1Q2hmoUdv/qOu6JTw1eqGflCOyo+2CbRAEb36Pt5CgYQz5vfZe4CGr0tnvO1ML8I/ObjxdkYQA4G0BC5tUmWTW5pPqtWSojP5urf/sG2KKpJhU+i4lcfFmcA6W38Ybje6gK6Jenfj6JDWV9/agDaKqzZ3TGAPqVCDRxUE3xM0pQ5Ibc0n1XLE+KHr34im3t6bER39mtHyBbaUfFTwUZ5BrB4KG7SL7/12fErjucG8M24gX//cb386WvCcHSLmjajgT41rm/4zGd3xQA83rvckawasjkhtzRUc0FeF5BM2LtckKwasjkhtzRUcwEDaA32Lhckq4ZsTsgtDdVcwABag73LBcmqIZsTcktDNRcwgNZg73JBsmrI5oTc0lDNBQygNdi7XJCsGrI5Ibc0VHMBA2iN/rkNtNdK2Ug+Jr05KKtFbmmo5kIfG4Ae/sHcCVqPxktc6JsHwTCAVpFbm2TZ5JaGai5gAK2BAbgg+Zj05qCsFrmloVojzLAPJgKyTL8bwHKBAbgg+ZikKXNCbmmo1ggMoHMwABckH5M0ZU7ILQ3VGtGo8Vf0gwEkI0EkmfBqyAcd/p51Ac3l+QGlxPjqwABckHxM0pQ5Ibc0VGvEABjAn6cp72n01xXH6xpAKTG+QGfhl31jAJ2mfAJAv7JvxV/fFwRv2a+mX/jUhiD4lf06//ew+i+edX0QvDVZGASXfOhwp5+3TAYQhrclmfAzamDo6JnxuHUvXgSeMRmRpcR4DAAAvGHfig+qUZ9V2vtPNugBoFfcnxuAmaUC5JOFazr9vOUyANWWqzyYxV0m/P3s+BXHCwYwZw0EbSXGV0b/GECFX7pjJJ+V05nhhNzSUC1S4zsP/2kSClOMgEkCYDZHC19S+QBTwbv0QoGBMLWkrX7cvCdxYfrXv20AZ8ft/qE8Mb4yMAAXJB+TNGVOyC0N1aIkGkwnP5ZCINV/Kh3eoJMC1MKRfjIAnQmfGkASDzyXnBwkmZHlxPjKwABckHxM0pQ5Ibc0VItMOHCks99LMfDqv3RWlg8WBBITwWpY8gxgUYdFlm4MxQB6j+RjkqbMCbmloVrUvgGsvL8vDGCJawD6ArCiJjG+MjAAFyQfkzRlTsgtDdWiNrqAUivojy4gdeuP+Zlf7y4gcwFYUZMYXxkYgAuSj0maMifkloZqkbq4e9mBZheBb4qix8bUnOEbY3t4dKxfrgFckwa+13kOIL/0u74mMb4yMAAXJB+TNGVOyC0N1SJ1BnCl7tk5mHXzxKcE2VAQZpY6ScgW9okB3FvMhC88CWwbQE1ifFUwHLQLko9JmjIn5JaGapG6BvDVnUGwST/4eyaeGr76CXssoDNbA3WKkCwMNh3oky4g0/HfS9i7XJCsGrI5Ibc0VIuyi8CtgwG0BnuXC5JVQzYn5JaGahEG0DXYu1yQrBqyOSG3NFSLMICuwd7lgmTVkM0JuaWhWjSgBiAB9i4XJKuGbE7ILQ3VXMAAWkPiXUBZbb0UpimSj0lkc0JuaajmAgbQGgKfA8AAOkNubZJlk1saqrnQzwbQ6NqAeQJg/p4qPwsDcEHyMYlsTsgtDdXaIU0JG1gDODu+vsrPwgBckHxMIpsTcktDtXYYZAPQYAACkHxMIpsTcktDtXbAANoDA3BB8jGJbE7ILQ3V2qEPDWB+txoS7n0PxZMza7+xPVz7dTNGkJ4TRc9sT8YI0l1AM0lKfFVgAC5IPiaRzQm5paFaAdPCnwo2J5Nndo7pwX9UDNjRYOXf6HkTK/f3jQGcHTcjvqlf/TNrbwnDK/4xGSVUnwfMmKVbooYG0Fn6sUAD6DTQGQAGlodVMvw+lfv+4nUbCxHxl24IVn3vjlWHX7xOrdIRy2gA0+EHjsenAZMmASaJAFY5AdNm9P91X4+XbleJMA0uAnf2TTEAAOgf/in4SNzY/3Lc5KupfcHbDs+euC/2gxN3BBtn47+xB3Tc/i+nASTMGANQP+5jAzBJwGu/peIiIz29nruA5CD5rBzZnJBbGqoVUflfp8d+/x13qfEhCvlgariIhR2XbVXBAH10DSBafO7Bu28OjQGolj+NAJ4Ob88zAa44jgGIQfIxiWxOyC0N1UpMrT52dOXfTmwzTlBICFYGEATq+kD/GMDi7qSJrzGA+NX5ydQA0pxgDEAAko9JZHNCbmmoVuLUyoOxB0yNnB7bVs8Ahv9sIj4F6B8DmA5HP3bPg8/P1DkDGP1MOqnBAKQg+ZhENifkloZqJU6PfXRic3R09ZdWHoxKEfHmzqBTwUj/GMD5yStU0OPiLtsArGsAetKAAUhB8jGJbE7ILQ3VSizs+NV33BWdGr5yJCpHxKcxkcG2PjKA0T36Np+CAcTz5neZu4BGb4vnfC3MLwK/+XiFH48BuCD5mEQ2J+SWhmpljgb6t3+wLSpHxCfPBpweW324Xwwg/o2fR79nXUC3JN3+UXQouwSQGkDFD4LJI6utuq9ZMZKPSWRzQm5pqFbm9NiI7uy/S70oRMSnf6eCjX1jAIuH4ib98lufHb/ieG4A34zb/febX/pPXxOGo1vUtBkN9KnxsMJOIPYuFySrhmxOyC0N1Vzomy6gHsPe5YJk1ZDNCbmloZoLGEBrsHe5IFk1ZHNCbmmo5gIG0BrsXS5IVg3ZnJBbGqq5gAG0BnuXC5JVQzYn5JaGai5gAK3B3uWCZNWQzQm5paGaCxhAa0i5DbRubcstRstIPiaRzQm5paGaC31jAMuZAF8HIQ+CYQDVIbc2ybLJLQ3VXBgMA6h43Ic6YAAuSD4mkc0JuaWhWg1p7mMT+t4ANBiATCQfk8jmhNzSUK0GDKAyMAAXJB+TyOaE3NJQrYY+NgC3BPh8ttnoocrqwQBckHxMIpsTcktDtRr61wDcEuCt2WajwnignYVfCjGATjM8AWBgOHHHqsMq/XdjMvnCpzYEwa/sV6/WPBys+D/0vOtWxDNe+FQQXPKhw+mS/Sfui9d8y/5OC+iWATglwNuzzUYFOvumGAAACONhleu+L1gzO/vidRtnf7JBj/m84v64mb90Q7Dqe7EBvHidWiVZsmY2WXJ4n37dcSp8twwgob0EeHv2TJVjQUd0Abkh+awc2ZyQW5qXqp0Ktqngl5UH9ZSd+qICfxd2rH7SjAY9FbxLL9mWLDEJYY8Gm4V2AbkkwBdmz1gJYVWAAbgg+ZhENifklualaibw/fffcVc0tfJgIfcx9oR46rKtuv3XyQDq9UhklpybGN70wBOR2GsALgnwhdkYgAQkH5PI5oTc0vxUbWr1saMr/3Zim3GCmuT3IFC/99M8sCBQOZDaJY7qfLAbjgk1AJcE+MJsDEACko9JZHNCbml+qnZq5cHYA6ZGTo9tq2cAw382oU4BMgNIM+Gj6PGd2hBkRkK6JcDbszEACUg+JpHNCbml+ana6bGPTmyOjq7+ko7/tbqA0tzHU8FIlFlDVLg19OXHtg7fKdQAXBLg7dkYgAQkH5PI5oTc0vxUbWHHr77jrujU8JWqk9++CJwawMIOfeV3+EZ10XcsXXJ6bOSJKDqzQ6gBOCbAW7MxAAlIPiaRzQm5pXmq2tFA//aPW/msp2f4Ljv5/fSY/lNcEnuFZkToNQDHBPh8dvUGIIO6tVX6TatE8jGJbE7ILc1T1fQNPgvmZs/ozM64kb/6icg2gLit32yWBJsOZF1AC1+OLeFNN0m9C0gcnu5dHSJZNWRzQm5pqOYCBtAa7F0uSFYN2ZyQWxqquYABtAZ7lwuSVUM2J+SWhmouYACtwd7lgmTVkM0JuaWhmgsYQGuwd7kgWTVkc0JuaajmAgbQGr26C6il2rr95Z2RfEwimxNyS0M1F4QbgB7eYa4wsudceZzPrgfCK3r0HAAG0D3k1iZZNrmloZoL/W8A3Y+DVGAALkg+JpHNCbmleamaedZ3ydyvxvSDATQHA5C740s+JpHNCbmleakaBoABcEy6Ibc2ybLJLc1L1Tpq/BVSDCAZ6SHJfFdDOuhw96wLaC7LBzBdQHrQh1uPR/UC4adH790dhuv2VPCFMjAAFyQfk8jmhNzSvFRtkAzgz9PM9zTa64rjDQ1gxor+qgmEn84D5W06C7/skQF0GNkJAIPBvhV/fV+a426Fv686bJLhX7g+CN6aLEzy32f3rfrep4JLPjx7Ip7ztsOzhWj4Vd+9Xi3qkAoNIAxvSzLfZ9TAz9Ez43G7XrwIPJNmQJ6fVK37/C71tzYQflrHx+uBozEAABgE9q34YJrjboe/JwZgZsVzrPz32AD+RE3+7h3q78ZiNLwZDXRjh0VVaQCqlVd5L4u7zG/3s+NXHC8YwFw2/rPJCzDUBsJP6/XnygbQEXQBuSD5rBzZnJBb2qCrNhUM/6kZ7L807n8y6v/maOFLalDoPP89nlx9IHpsTP19NFh9rBgNP3IsOipnOOi01Y8b8iQOTP/6tw3g7LheRb9SvTyX3/q8mV0OhDdmUO3FYQzABcnHJLI5Ibe0QVdtSo/4rOK+Sslf6j8VCWyw8t/NJmk+/OpjdaLhxURCpgagM99TA0jif+eSkwMzxL9+tWiyX9Z9q14gPAYgBcnHJLI5Ibe0QVdtSjXZOvC3lP1r2vY089HKfzeb5NEANdHw8V9pBtDwDCDr0087hJ67+xodGFYbCI8BSEHyMYlsTsgtbdBVa9cA4tUbGYCVGSzHAJa4BqAvACusJ4HPKquoDYTHAKQg+ZhENifkljboqrXcBbQ536RsAKVoeFkGkN26U+8uIHMBWKFenR1fpx4YeDrUBlAOhMcApCD5mEQ2J+SWNuiqTQWXHWh2EfimKHpMh75n+e9lA6iJhhdlAKPXpIHudZ4DyC/yrs8uAie3+tcGwmMAUpB8TCKbE3JLG3TVpoavTHp2ShHvSfR7MsdaWDaAeqHxggzg3mLme+FJ4LIB6DXC9+vnhmsC4btjAAwH3T6Sj0lkc0JuaYOu2tTKr+4Mgk36wd9C+LtpzM9sDdQpQmTlv9cYQDkaXpYBfGbptXrHoO9d3UGyasjmhNzSBl215CJwxUh7DkAqg753dQfJqiGbE3JLG3TVMIBeMuh7V3eQrBqyOSG3tEFXDQPoJYO+d3UHyaohmxNySxt01QbbAKQz6HtXd5CsGrI5Ibc0VHMBA2iN5b8LqI3auve1O0TyMYlsTsgtDdVcwABaY3a5vxoG0G3k1iZZNrmloZoLggzA3OA/f0+U/c2piYYsr1BL1c8BVPhmrYABdBu5tUmWTW5pqJZgpYQl438qXq4fHybNAEyzXdN4lw2ghdYdA+g9ko9JZHNCbmmollDXAB5550HpBqDBABIwgG4jtzbJssktDdVqyQ2g0T1EGEBrYAAuSD4mkc0JuaWhWi2SDSBu0795SKW4L+6O/x43XUAm4j0Neo+e2W6PCWSG+7n1eJQlwc/vVkMCvU8NCTQ9em/8Put0UuT8zfE7PosB9BzJxySyOSG3NH9Vm1r95M5g+MZoYWeghwk1w/78XvziR4kBLOwIgmAkHw9o+AY9EtDI0WDF/ioradMAblHt+G27kta81gBmzPhvWxIDmMkGCU1WUAOBpoOClsYHDcPfKRtAZ+nHyx3PThw8ALRCKfvdCom/dMMavYbOgF9jLTDB8JduCOI5ndCRAYRrH4qejhv0h6KnVKtecxF4Llz39fjn/Pbwdm0A5ydV6z6/y7TxJgXmA/HpwPykGSA6zROI/8QnFE+Pl0PhOxMZAwAAiewLVu2f/c4G9ffbcYuu2vm4xX/7YTVvTbrOivuNM+xTC07cF3xEucLGTj+6MwO4XUWAqfZcBYHVGoAZ1jleuD41gD3pxoUO/hljANdqz1ivk8T0XLqAeo3ks3Jkc0Juaf6qVsp+t/LfTwX2NYB6wfC9vAag2nfTuV/XAPIMgCQaUvXyXH7r81GUG8Dicw/efbPuLspDAOZMy89F4N4j+ZhENifkluavajXBX1n6Y54UmRtAMRi+pxeBv9XUAM5PpgaQhMMvmtSvdd9KV1OXj8PkeoFtADo0GAPoPZKPSWRzQm5p/qrmZABJMLxgA7Bv/Uynn7v7mlD9wE+vAYx+7J4Hn5/hDEAoko9JZHNCbmn+qlbXABp2ARWD4QUbQNy+315cWXN2UifB61ME3dm/uKtoAFwDEIPkYxLZnJBbmr+q1TGAaCp417HosbHcALIA4WIwvDwDePPx9O9cOHpbvMbXQnMR+Oz4OnXD/9OhNoB4BXNVeH57qQsoNo71x6Nnau4C6ggMwAXJxySyOSG3NH9Vq2cApqvnquxBsKPpcwClYHh5BpDe4B+/OJReAoiyi8DWrf7XpjN0p5BlAA2eA+gIhoN2QfIxiWxOyC3NX9XqGYB+3it7ECyKzm0NVj9ZJxhemgFET+lf7uavefRXPSWcPwkcvl+dBpgVFg+Nq/uCnlV9PpYBRPPq2eKKrwF4u3d1gmTVkM0JuaWhmgvSxgKSCnuXC5JVQzYn5JaGai5gAK3B3uWCZNWQzQm5paGaCxhAa7B3uSBZNWRzQm5pqOYCBtAa7F0uSFYN2ZyQWxqquYABtAZ7lwuSVUM2J+SWhmouYACtsWy3gbrUVvm3rQrJxySyOSG3NFRzoZ8NYOlg+OpYrgfBMIBlQ25tkmWTWxqqudDHBlDtjf5LgAG4IPmYRDYn5JaGanVT35cAA2gNDMAFyccksjkhtzRUwwC6BwbgguRjEtmckFsaqrkg2gBm1n5ju8qNzMPhrez3JBfYDoO31ss2rQgMwAXJxySyOSG3NFRzQbgB3JJnwOtweGtAuNQA8hHi7PXSTS06C79cpoxeooABoMC+FX99XxC8Zb+afuFTQXDJhw6rSPdV370+uOTD8bwT920wi3Xob7xK/PJX9hdWsbdb83CwYn+2TWd02QBMu26Fw1vZ73koTDrDXi/ZFAMAgP5m34oPqhGcL7lzdvYnG/RgzmtUQ26SvTaqUPhksTaAZBUdAp+tYm936YZg1eFsm87otgHocEcrHN7Kfs8MIJthr5dsWhl0Abkg+awc2ZyQW9rgqjYVDP9ptPBFFe6ic17iyW3Rwo5g5Fh0NJ55bkJd+n002BwlUTBXHzNr56sUt9sc5dvI7gLSkWB2OHxx2H9tANmMwnozeZpYJWAALkg+JpHNCbmlDa5qKs1L3eKz8mCS9LiwY0S/NA35uYnhTQ88YearV/pOILU4X6XOdsk2fWAAdjh8MwMorIcBSEDyMYlsTsgtbXBV04EvUXR0+K4s0j2NfdF/j+qIrxuOFUJ/47XzVWq2y7bpAwOww+GbnwFY62EAEpB8TCKbE3JLG1zV6hiA+nmfGUD0+M68cW9uAOl22TaH5RuAHQ7fzAAK62EAEpB8TCKbE3JLG1zV7C6gzelM2wBiXn5sq2nxC11AuQGUtsu2ubMPDMAKhy8agEqOt2bY62EAEpB8TCKbE3JLG1zVpoLLDqSXdYdvVBdvx0Yiu3UfeSKKzuxIfvLbF4HzmODSdtk2/WAAVjh8Ofv92kIWsLUeBiAByccksjkht7TBVW1q+ErTfaMabt2TE58SWGcAU6Z7J2nc665Snplt0wfXACIrHL7Q3utgeHuGtV71BsBw0O0j+ZhENifklja4qk2t/OrOINike27OqK77TQcKXUALX46b9zfdlM5Tqwxf/USxl6i0XbaNaAMQxODuXd1EsmrI5oTc0gZXteQicFfAAFpjcPeubiJZNWRzQm5pg6saBtB7Bnfv6iaSVUM2J+SWNriqYQC9Z3D3rm4iWTVkc0JuaYOrGgbQewZ37+omklVDNifkloZqLmAArbE8dwE51lbtV60QyccksjkhtzRUcwEDaI1leQ4AA1hO5NYmWTa5paGaCyINYM6M5azyvUb/qIUVM6atEID5e6opxoABuCD5mEQ2J+SW5oVqybCetZybUAteTl61Hg4s2ACSfK9m4/o3MYCKE4MxABckH5PI5oTc0rxQrbkBPPLO5GpxnxuA5ux4eFt8FjBuDfG5JBiALCQfk8jmhNzSvFCtuQFktwsNggHokK/iGM9LggHIQvIxiWxOyC3NC9UaGsDCjs19ZQCLu5IYd9VMm8ZadflffptaqHt2TPdOnXZ8evTe3WG4bk+6YrR4aDy5VhAbwFPjetHMUr1HbYIBuCD5mEQ2J+SWNuiqnfm9ILj6R9oAzuwcywb2+dHWYPjGeN7U5oUdekQ4hTaAMxMrD1grZFsZdzilkiEXdqw6XEFpGW20YHq0tsVdKr4xbsdvz7r8t0S2ASQ+UWDarKgWJOtk1wqmR/8gWVTHADpLP16OtHYS4QGgLibW/dINa2brx74nMfFrZs3kqsMvXqdj4su58PFWD6ss+H1q1Rev29hZVe4GkI7onI7xPBeu+3oUzW9XXpAbQPkqrzGA0T2q1V+fLJ4J1z0Uv5MyhHTRtfVOHTr7phgAAPSKuCV/++HZ72xQzfa+4G2HZ0/cF0/Gc9ccnn04afZn98Wte7L2qu/doZp5a4V8q38KPhIv+OV4ZTXVEe4GsLgr/u0/N/oXcYN/fjJLe4+n19tt/4w+IygbgDKFudQAss6kawuLuAYgAMln5cjmhNzSBlu1pPP/VDAS1Y191ytZ1wAu25rFiKXR8dlW6oLx6bHff8dd8Qb39+wisGrxp9d+Y/LapBlPE92VL2QGcH5S/byv3TDLALvW2EftIgxAAJKPSWRzQm5pg61aEupo2u46qb96jmUAQaD6+KPaVMh4q2hq9bGjK/92Ylv8br27CyhuvBd3rVcnAqrZjlv6MMvzsnp/Fg/VXMvFAErI3fElH5PI5oTc0gZbtXYNYPjPJtQpQH0DOLXyYOwBUyOnx7b1zgDOT64/O35tNLP2m6ozqHC7Z6H7/5nx0nVgDKCE3B1f8jGJbE7ILW2wVWvUBdTIAFYfU6taBmBtFb/ZRyc2R0dXf2nlwR4+BzC99u/0xd8/HNc38FgZjpkB/H+q+6ec7lgygOQagH6NAchC8jGJbE7ILW3AVZsK3nUsemxMter1Yt/NOrrfP0qD4INtxeTgdKv49a++Iz4PGL5ypJcPgs2NXhP/6j8/qW/ojNvy0duiaPFroXUReE4tmp9sfgZg7gKa36VsomAAbz5e71MdwQBckHxMIpsTcksbcNVMrPtV6kSgbuy74mjhOYDTY8oF6uTCqxXj84BzE7FD9NAAzqpMd3X113T+HMouAeRnAGZe6ad82QCS68eqI6hgABU/CMZw0O0j+ZhENifkljboqqlY9+xBsDqx7zHntgZmysyZCjaXc+H1VkmH0sKO2Ax6aADmfv1oOm3gn74mDEe3qJ/t+TWAp8bD0Q+UfsmXDcA8Caw3tBapTSvsBBr0vas7SFYN2ZyQWxqquSB3LCBZsHe5IFk1ZHNCbmmo5gIG0BrsXS5IVg3ZnJBbGqq50BcGMBda3L70+l2AvcsFyaoh0dSBvgAAIABJREFUmxNyS0M1FzCA1mDvckGyasjmhNzSUM2FvjAAAbB3uSBZNWRzQm5pqOYCBtAa3b8NtIPaqvuaFSP5mEQ2J+SWhmouyDGAekP/K8ytnqWI9/aCwipgdujnugsGsNzIrU2ybHJLQzUX+sQAygM7YAB2bdV9zYqRfEwimxNyS+s/1V5uJ7uxdVp/z9NjPRwNtEwjA9BgAM1qq+5rVozkYxLZnJBbWt+p9sg7D2IAORiAc23Vfc2KkXxMIpsTckvrO9XU2J3+GsD87mvCMHyfHu5z7Te2h2u/vmv03kPJnHJCfJrwq0eLuPV4ZgDqtVkrX1TYuDIwABckH5PI5oTc0vpONa8NQI3Vlka7z6y9JQyv+Mddo9ekc8oJ8YkBzGSZYdoA0hQxNQxcvqiwsU1n4ZddN4DOygOAfsJEuOvw3uCSD6s5L3wqCC750OFs+arD5m/833evr1ln36rvfUrNPBHPedthlQf81/cFwVv2p1u+8KkNQfAr8UuT+/vidWsKn/DCv4k3++6GNR1+C2cDmA7VGG/zk2Y8Z9Xox825SnSfNuNBlxLidRfQeT0w9LzqKtIGMKM2iJ4Z16kw2aLCxhgAAAgkNYBfvF4N0rxxdvYnG/RwzWuy5bkB6AWldfat+hM1+bt3JIv2rfigmrrkTrNlsuaK+03Tr23A2tpMXto7A0iYMQagx/TUI/rHTXnctNckxKcGsCfZThlAetHg7PgVx61FhY0rgy4gFySflSObE3JL6zvVTBdQsPpA9IiJeXmXDmzZZpbmAznH64wci46W1plSGz42pv4+Gui8l+E/tVJirPgXnROm/uRbx2+ZxMv07iLw4nMP3n1zaAxAtfzpdd3p8PbahHhzEXg6nnP5rc8nK6dZkHpDe5G1cWVgAC5IPiaRzQm5pfWdasYAVECLimtMMh8XdphIl4IBqBa8vM6UifxVi0yLr5Ne0pxIOwDyVNzimyzhbOs8YLJXBrC4O2mlawwgflWbEG8MYNEExKz7VskAVJ9QtqiwcWVgAC5IPiaRzQm5pfWdavlFYBPabjp6kkiXggHUW2cqa/oTAzB5wElSvB0Brxp/ZQLW1nnEfK8MYDoc/dg9Dz4/U+cMYPQztQnx2W2gz919jYoIqzkDKC6q7AtlYAAuSD4mkc0JuaX1nWoNDCAJdm9qAKo/p2UDiI6uPDilo+CzrXtuAEnjbULBUgOwrgGUE+Lt5wDOxtuWrwFYiwobVwYG4ILkYxLZnJBbWt+pVjaAzYWlecdP/XVqDKBhF1Dc8n90QkcJZ1v3vAvIXLSd317oAlI39czvMncBlRLidcT72fF16iGBp0NjAPZdQNaiwsaVgQG4IPmYRDYn5JbWd6pNFVLdF3YM3xhPPjo2ki4ONutrxMfqr1NjAMFlB+pfBI5nvGlsm84Azrfu9UXg6bSjfr3VBXRL1nNfTog3Ee/T2bMDNc8B5IsKG1cGBuCC5GMS2ZyQW1rfqXY0CEasFjzpodE/5BWn1KvLttoGYK9TewZwZdI5VPtuR02/kjXPTF7VuwfBVJB7ePmtz6rum8wAvhk33e833TmlhPgk4l3NDd//UKMngfWiwsaVwXDQLkg+JpHNCbml9Z1q57YGq5/MW/DozM64Td50IFv+yFiw6cnCNQB7ndprAF+Nl20qrDl89RP6nZIeH/sT1OTVP5I0FpBs+m7vEoFk1ZDNCbmlea5achG4LqfShwtqwQBaw/O9yxHJqiGbE3JL81y1JgZwpsngQBhAa3i+dzkiWTVkc0JuaZ6r1tAAzk0Eweb6iyIMoFU837sckawasjkhtzTPVWtoAAs7gqsbb4YBtIbne5cjklVDNifkloZqLmAArdG1u4CqqK2C9+gOko9JZHNCbmmo5sJgGUApOr5CuvUcAAbQM+TWJlk2uaWhmgv9ZwBqBIh0GKAS5eDICsEAXJB8TCKbE3JL81u109kTxDW83Gw7DKA1MAAXJB+TyOaE3NL8Vq2xAajs+cb0nwE0AQMQhuRjEtmckFua36o1NoBmT4j1owHUpMn/32myQBod30+h8BhAz5Bbm2TZ5Jbmt2reGEBtmvzTZQPop1B4ooABoBOsfPcsCT6evD4I3ro/iR62lpy4Y83DwYr9J+7bYCLkO2P5DaA2TV4zo8YENV1AfRUKjwEAQAdY+e55EnwyueL+xADyJSfuuHRDsOrwviCJkO+M5TeABCtNXjGnB39OkoP7KRSeLqCeIbc2ybLJLc1T1fJ898gKAYjnbo4WvqTT43UmfHFJZAJjHg02910XUG2afKRaft3oawPor1B4DKBnyK1NsmxyS/NUtTzcK7JiwFQOcIIyAGtJmjQ2vOkBNVZ03xlAbZq8+rFvJrQB9FcoPAbQM+TWJlk2uaV5qloe7xtZQcBW/KOJA86WmLQAlUYTBMM3HOs7A6hNk1e/+U1vT3IG0E+h8BhAz5Bbm2TZ5JbmqWqOBhA9rsJhgtWH+8wA6qTJmwvAiuQaQD+FwmMAPUNubZJlk1uap6q13wWUhQO8/NjW4Tv7zgBq0uTnsr4eHR3fX6HwGEDPkFubZNnkluaralMNLgLfFMVzR0z2fGGJMoDYNp5QSTF9ZwC1afL5Rd/1Jjq+r0LhMYCeIbc2ybLJLc1X1bJ89zqp72pKZc9bS5IzgCn9Oui/TOCaNHnLAJLo+L4Kha+itgreoztIPiaRzQm5pXmrWprvHhWS4M9sDYLLVP67yp4/li9JDGDhy7ElvOmm/rsLqEd4u3d1hGTVkM0JuaWhmgsYQGuwd7kgWTVkc0JuaajmAgbQGuxdLkhWDdmckFsaqrmAAbQGe5cLklVDNifkloZqLmAArcHe5YJk1ZDNCbmloZoLGEBrdOcuoIpqq+ZtuoDkYxLZnJBbGqq5gAG0xmw3vhoG0Evk1iZZNrmloZoLvTSAOT2a8/w9UfY3p8mIPtNZCEDyDnUX1bxjZ2AALkg+JpHNCbmlDZBq1nANVtKXmrSW1KVuAHyzVPieG4AZv6cmzbdjA6g4HxgDcEHyMYlsTsgtbYBUczaAugHwTVPhe94F1JEBNFmEAQhA8jGJbE7ILW0wVWvPAOrm/zYNBcYAWgMDcEHyMYlsTsgtbTBVGyADiNv0bx5SWe0q02XLcdOBM1OIc4+e2R6Gl98WpQagB/W5VQ/qo8YAGv0jNRW38k+Nh+v2RGkXUL1F2TtWBAbgguRjEtmckFtaP6s2tfrJncHwjdGCGtbnWNLMn/m9fIyfdDJZslOHuaRbL3xxLAj+1QE1BGgQjCzsGDkarDwQPXqlXimdqbbTf4uftbwGcItqlm/blTTOtQYwY0Z125IYwEwe7ZgM+aaa9OnRP1CT6se+NoC6i+oYQGfpx91IbycRHgBmZ/et+hM1NufvqgT3YOPsiTtWHa4Ne1eT1pJgTbZ1EvBuAuCT1PeHzXCfG9OZ8XZm69JndUZ7BhCufSh6Om7QH4qeUq16zUXguXDd1/Vg/7drAzg/qRr5+V3q70y47qEk+nc6HN2TJMLMGdeot6i2C6izb4oBAEB32Bes2j/7nQ3q77fjxls11HHD/fbDap5q9a1J1YSrlyfuCz5iNn7xOtW4f1s15vtW3K/W3qhmrtgfO4daZGbmBlD8rM4Kb9MAbteJLp9Rf+Mf+DUGkHThn59cnxrAnmxbvUgHQU7rX/ZzauBn9Q4NFnENQACSz8qRzQm5pfWzajq0xcS1q24a9Z+V9GVNWksWdiRXB7KAd9Pdb94m5scPfPpKNRK0mZl3ARU+axkjIU1LbTr36xpAPrL/Fcf1air85fJbn4+yJEiNsQm9hXqHBoswAAFIPiaRzQm5pfWzalNp028bQDnrV02aJaZ3J0ivB6cB75HV1qcRMHUMoPBZy2oAqulvYgDnJ1MDWPstvdqiyfZa9y0MoBlyd3zJxySyOSG3tH5Wzc0Asrt7koB3u60/NxFcdcNXHp/oJwOwb/1Mp5+7+xoV9YUBNEHuji/5mEQ2J+SW1s+q1TWAxl1Am2vfQQW832W19aeCbVFkwuDzfqHspUwDiKZ1yLu1subspOoRypv2kgE0WIQBCEDyMYlsTsgtrZ9Vq2MAxbD3dNIsHb4xXvHR9AmBLOD9Lt2/nxrAyDEVC5l2+k8Fm9VtohIN4M3H079z4eht8RpfC81F4LPj6x6Kt3s61PG/8fS8voxcMoAGi8w7VgYG4ILkYxLZnJBbWj+rVs8AasLer0qfA7Cy383WScC7CYDPuoDSbiKdCn9Kvbhsq0QDUPfrm7/RofQSQJRdBE7u+E8uEKvenrIB1F+UvGNVMBy0C5KPSWRzQm5p/axaPQMoh72XHgQLNh1It04D3k0A/JPmIrAKhX/TTerXv0mFf2Qs2PSkwGsA0VPj6q5N89c8+rvluP0kcPj+h/TW6nFftaTGAOovSt+xIvp57+odklVDNifkloZqLvR8LKA+gb3LBcmqIZsTcktDNRcwgNZg73JBsmrI5oTc0lDNBQygNdi7XJCsGrI5Ibc0VHMBA2gN9i4XJKuGbE7ILQ3VXMAAWoO9ywXJqiGbE3JLQzUXMIDW4DZQFyQfk8jmhNzSUM0FDKA1Zof+c/VgAL1Ebm2SZZNbGqq50EcGoMZ8sAf+WVYwABckH5PI5oTc0lDNBQygNTAAFyQfk8jmhNzSUM2FPjKAnoIBuCD5mEQ2J+SWhmouiDWAmgj5aH63GhjifWpgiOnRe+O5OhfemrQy5aOZtd/YrhIoqwIDcEHyMYlsTsgtDdVckGwAxQh5NbpbOjTcdN1JK1M+NoBbzPhwOZ2FX3bFADorCQCgMftWfe9TwSUfnj3xqSB4m0r/fSGeuORDJh34u9erRR3STQMoRchPhx+I2/P5SZP9a8Lf1xcmrUz52AtMvjAGAAB+sm/Vn6hRoH/3DvV34+zsTzboIaLXKAMwo0Vv7PATumoAxQj5hHL4uz2ZZ8qb1SqELiAXJJ+VI5sTcktDtRqmgtUHVJBM/PfRIM2YWfhisE1lw4wci44GI4K7gEoR8vGL5x68+2bdHTRdLwTMzpSPDeD2JT6gPTAAFyQfk8jmhNzSUK0GnQVm0iCtlMmFHSN5ROQy5gG0R016gLoYHCbXA+oagJ0pjwGIQPIxiWxOyC0N1Woo5cxkafJp5MzyBsK0R40BTIejH7vnwednmpwB5B1FGIAIJB+TyOaE3NJQrYZGBrDyYP8ZQPII2OKuhgZgZ8pjACKQfEwimxNyS0O1GmoMYHO6pB8NYHSPvsWnsQFYmfIYgAgkH5PI5oTc0lCthpqo4eEb48lHs2zhfjKA9Ib/MFzfyACsTHkMQASSj0lkc0JuaahWQ8kAoqQPaPiufjQAFf0eXn7rs+PqmYD6BpBnynfBABgOun0kH5PI5oTc0lCthrIBRGd2xu3/pgP90AUkDPYuFySrhmxOyC0N1VwQ+xyAMNi7XJCsGrI5Ibc0VHMBA2gN9i4XJKuGbE7ILQ3VXMAAWoO9ywXJqiGbE3JLQzUXMIDWYO9yQbJqyOaE3NJQzQUMoDW6cBdQdbVV9k5VI/mYRDYn5JaGai5gAK0xW/lXwwB6jNzaJMsmtzRUc6G7BjBdMyi/Ys5hqOb677R8YAAuSD4mkc0JuaWhWg3mbn9NMhJoDRhAa2AALkg+JpHNCbmloVoNQg3ABQygMXJ3fMnHJLI5Ibc0VGsGBtAZGIALko9JZHNCbmmo1oweGsDZcd14P7M9DC+/Tc2dMwN63rs7DNft0evlywrM774mDMP3PWTe6alxs7q1ZWFMoPz95reHo3+UDA9UERiAC5KPSWRzQm5p3qq2sGP1j7YGwzeq6TM7x8xgP+kSNQDQ7wXB1T/SBqDGAhq+QXcLPXqlnuy+ASTt/4wZqXNLlBqAeV1eZnN2PF9nevQPskl7rhUGkM012/1O2QA6Sz+uPMCdRHgA6Bwr4D1JfV9xf7pk1eFk3qUb1tih8LMPL0sofNxAz+tY97jRX/d1/dP89swARveoeJf1xWWFrcMPHI/nT9qrF7csGEA6N/6z5Xj0tB43GgMAgIEmNoA1h+MWPW7X9wVvOzx74j7dxM8aA4iXvv3w7Hc2mMVv14s/MvvidSv2x4ZwXWwQHbGkAfxH0/6nffjnJ9dnBnCt9oX1xWV1mCmvXt4yMYB07tnxK8yA0HQB9RrJZ+XI5oTc0rxVLQt4P6b+y2ZEhST4U8FIZIXCx/z4gU9fGXR5OGjVL5MmOSbEL+eKoV6FZUUWn3vw7pubZIDVnUzuMuUaQO+RfEwimxNyS/NWtdrMx6PDd2VLknnnJkYiKxQ+ne66AYxu183x+cm0kV/7rbIBFJbZLO5O5mMATZG740s+JpHNCbmleata+waw8uC5ieCqG77y+ES3DWD0M+YasEn2Sqg5Ayg1/NnW4ejH7nnw+RkMoDlyd3zJxySyOSG3NG9Vyw1gyS6gLBT+VLAt0t1GXTeApC9+2rrCO1du0evHN56fTHuPGhuA3nKmeDmAawBikHxMIpsTckvzVjUr83EquPpYtPDFYMRaMhW861j02FigkuCzUPjYD45FZ7Z2vQsobpXPT5o7f0Zvixvzr4Xraw3AXmZxfnJ0j745qLEBzKjLBs+MFw0guQtoHAPoOZKPSWRzQm5p3qpWN/U9X2LmXaVOBPLF5yaSzqD7u/8g2Jzu2z+Ud/OXDcBeVtg6vTbQsN/HuuO/Zm7NbaAdwXDQLkg+JpHNCbmleataOfV9+OonCkvUPOtBMPOcWPzrP3jTTVPDdy7DUBDmDs2nrwnD0S2qc6bGAKxlNouH4pb88lufVV069Q0gOps982vPVWcN6/5LtdcAfN27OkKyasjmhNzSUM2Fwc0DcBlztDHsXS5IVg3ZnJBbGqq5MIAGcHZ87UP1HizuCPYuFySrhmxOyC0N1VyQZQBzoYVr+50+WFZlDxB7lxOSVUM2J+SWhmouDKABJNcOagcX7QT2Lhckq4ZsTsgtDdVckGUAcmHvckGyasjmhNzSUM0FDKA1qr4NtNLaqnyzSpF8TCKbE3JLQzUXZBhAq3lfLQ7v0IX4sNmhn6sSDKD3yK1NsmxyS0M1FzCA1sAAXJB8TCKbE3JLQ7WlObc1CLZFL1tzMIDWwABckHxMIpsTcktDtaWZUuNAPPLOg/kcDKA1MAAXJB+TyOaE3NJQbUnMMKFTKyUaQBr5XkyCt4Lebw7DLc+Or1/clcQI65EgtCHkkfIza7+xPVz7kJ1FXxEYgAuSj0lkc0Juaai2JGZsIJEGkEW+F5Lg6wW9z6gHBBZ36fiwOTVtRcrPrL1F5YpZWfQWnYVfVmwAnRUDANCAF64PgrfuV5HAax4OVuyf/fYvBcElHzo8+09q/M9/eYeOhT9x34YgeMv+Tj+qKgOwI99rk+ALQzynA36qsUNVS29Hys+EZvS5LIseAwAAr/jJBtXQr7g/NoBLNwSrDj9sRn7eWDCAfXreJXd2+FmVGUCW855QSoK3Ql4Wd6lY4dG/iBt8HRljR8rPmAHg8iz6yqALyAXJZ+XI5oTc0lDNsLAj2BwtfCnYZqaicxMrD0TRaRUWlncBmeywR4PNUrqArLGh6yXBJ96QDfw8vfYbk9fqAT8LkfIzZvyILIu+OjAAFyQfk8jmhNzSUM2g0n8NWTTkjx/49JVByQCGNz2gYgPEGUD9JHg75zeeXty1Xp0IJIFjeaR8ZgBJFn11YAAuSD4mkc0JuaWhmiFP/jXtfZoDVjCA6KiOBrvhmDgDqJ8Eb58BnJ9cf3b82mhm7TdVZ1AhUj41gDSLvjowABckH5PI5oTc0lDNUDaAcxPBVTd85fFiF1AUPb5Tu0J3M4FbJW/pGyTBF4Lep9f+nb74+4fj6le+HSmfG0DFmfAYgBOSj0lkc0JuaahmsLuAVHt/Ktim55YMIOblx7Z2ORKyVWwDaJAEH65P49+judFr4l/95yfNLT92pLxlACaLvjIwABckH5PI5oTc0lDNsLAjuCmKHhsbyQxg5JiKAM4NYPiu+DRh5IkoOrNDnAE0SoK34t/V9Hp9kmA6f6xIecsAkiz6qsAAXJB8TCKbE3JLQ7UE0+cft/JZF5Am/t1vZhwNghE1KIRiRNw1gAZJ8NH87jDcYiZN/5A6KTBb55HytgEkN5FWBMNBuyD5mEQ2J+SWhmop8c/94LID2UVg9fJNN03ljqDOBha+PKbmCrkLSD7sXS5IVg3ZnJBbGqq5gAG0BnuXC5JVQzYn5JaGai5gAK3B3uWCZNWQzQm5paGaCxhAa7B3uSBZNWRzQm5pqOYCBtAa7F0uSFYN2ZyQWxqquYABtAZ3Abkg+ZhENifkloZqLmAArTFb7VfDAHqP3Nokyya3NFRzYVAMwAwJNH9Pt94fA3BB8jGJbE7ILW3wVXs5u7W/OdZKp8dGmi4fLANoMTLYBQzABcnHJLI5Ibe0gVdNhbljAA3AACQi+ZhENifkljbwqk2txAAaggFIRPIxiWxOyC1t4FXDAGqY331NGIbve8gYwEySIxM9sz0ML7+t0k/CAFyQfEwimxNySxtw1RZ2qLHb4rb7yR3B8I1qzpmdOtIlWyN7bRr4M78XBFf/SBuAtWTkaLDyb+Ll6SDRa/rZAMwQoaEaFto2gBkzd0tp7c7Sj6uNcScUHgDa4IQOcz9xxy9ebxLek+z3YE26Qv76xB2rDicvL92wprhExcR/L17+4nVqw38KPtJZVaU2dpkNYDr8wPH4NGBSRwNnXUBz4bqv60iBUh5AZ98UAwCAHrJvxf3KBlbtn/1/VGO+L3j74dkT92VNeP5aGUC8YvzyOxuKa8ZzNyYGod7N/OmEUovcm2sAM0UDMINBq+TICj+DLiAXJJ+VI5sTcksbeNXMNYDhu0y8V9K7v7Aj6eS3XqsuoOTlqWCkuESlgekuItUHdG5CSB6AM4vPPXj3zWHBABZ3pZEyOkWyIjAAFyQfk8jmhNzSBl61/CKwaeFNpEuQXNC1XpvFOhlY5UOWliQGoJbEJtDXBrC4O2nqbQM4P5kaQLWJYNW9V4QBSEBubZJlk1vawKvWwACShF/rdSMDMEvSi8RHVx6M37GvDWA6HP3YPQ8+P1M6A6iy4U/BAFyQfEwimxNySxt41coGsLmw1Hpd0wVUWJL+PT320dgd+tkAzk/qTh6dEGlfA6g0DT4BA3BB8jGJbE7ILW3gVctDHdXfhR3DN8aTj6Y3+luv9UpTwbuORY+NBSM1S7K/bxrb1ucGMLpH3+6TG8Cbj6u7gEZvi23hayEXgXuN5GMS2ZyQW9rAq3bUPAeQtuBJz466KKzJX1uLr1L+UFqSPgh2VPUe9bMBxL/1E9anBqCfAzhU/SUAhoN2QvIxiWxOyC1t4FVTYe5PWi24erwr2HQgW569zhdbD4JZSxID0L1EfW0Ai4fiFv/yW58dv+K46fl/alz/7H/6mjAc3VLhPUAe7F1dQbJqyOaE3NJQrU30w8B9bQDLCHuXC5JVQzYn5JaGau1xRp8HYACtwd7lgmTVkM0JuaWhWjucmwgCdXMQBtAa7F0uSFYN2ZyQWxqqtcPCjuBq9X8MoDXYu1yQrBqyOSG3NFRzAQNojeruAupCbdW/ZUVIPiaRzQm5paGaCxhAa8wO/Vw1YABSkFubZNnkloZqLvSBASzuMsN7toB6KGx9pbd/pmAALkg+JpHNCbmloVqJljLkB8oA9ECg3UmFxABckHxMIpsTcktDtSKtZcgPlAGcn1zb6rlCu2AALkg+JpHNCbmloVqRqWSY0OYMmAFUGQFQAANwQfIxiWxOyC0N1YoMkgHce8hEv0d24PvM2m9sD9c+ZALA1ECgM+kIQF0JhccA2kfyMYlsTsgtbQBVO7NzLB3t58zWIPjXhSlrXJ+plV/9YhD8K7340St1AHyWIX/Mepv45Y+2mkj5hS+O6S36wwCuSaLfC4HvM2tvUalftQbQnVD4qgygszIAwBOSMHeV22smC1M62TfN9/2gmnnJnbOzD5vsl41phnwWD282SZaqnOB0i45YHgMIR/dEi9Pq8q4d+D5jLCE3gKQLqEuh8BgAACwj+4K36TD3NSbM/cR/SmPdk6ncAIJL/r1Z8cXrVuyPTeK6JPU9WWq9zZrDsUeo9dTG31ZW0BHLZACqKT8/Gf+4twPfVRBYPQPoUih8VQZQYVFpbdW/ZUVIPitHNifkljZwqqno9yjSQe4q2TGdmU7ZXUA6FMAkvkc/fuDTVwa6X8jcBWS9jVlDzTg3MbzpgSeiPrkGYEb2nw5vLwS+z5if+GUD6FYoPAbQPpKPSWRzQm5pA6daGuV4dPiuPNUxnypcAziYrJiGv1gGYL2NtclRHRFzw7E+MoC4wS8EvjcwgG6FwmMA7SP5mEQ2J+SWNnCquRjAuYngqhu+8vjEkgYQPb5TG8Xh/jGAuKUvBL43PAPoTig8BtA+ko9JZHNCbmkDp9qSXUBpf47VBaRDXpKZ9bqArECwKHr5sa3Dd/aDAVjXAKwru6kB6P/NhPk1gO6EwmMA7SP5mEQ2J+SWNniqTQVXH4sWvqhz3YObouixMXsqXrxZvdQB8JcdMCueCkaOqRtFE1dI4uHzt0kN4PTYyBMqE6ZPDGB0TzS/y9wFlAe+JwYwo/r6nxkPrbuAuhIKjwG0j+RjEtmckFva4Klmxb6byeLUKTVx2Vbd1l+ppuOf/CrnJZnMMuTrZsJPmfVG+uMawC1Zl74V+J4YgEqCD8Pfye8CEh4KX2FRaW3Vv2VFSD4mkc0JuaUNoGoqzH346if05NZA/cwvTD0yFmx6MnkQLF5z07Fk6ZtuUr/+8wz57G0sA1j48phasU/uAvpm3Ka/39zTkwe+JwYQnd0ejv6RdRsoofBikKwasjkhtzSPVWtt1Ie69IEBiMDjvasDJKuGbE7ILc1j1TCAruPx3tUBklVDNifkluaxahhA1/F47+oAyaohmxNyS/NYNQyg63jFr6BiAAAYZUlEQVS8d3WAZNWQzQm5paGaCxhAa7B3uSBZNWRzQm5pqOYCBtAa3AbqguRjEtmckFsaqrkg2wDsMLC58Fp7mAd93+fyMTv069WAAUhBbm2SZZNbGqq5gAG0BgbgguRjEtmckFuap6qdHhtZcp2XrQGASvSPAZjXGECd2qp/y4qQfEwimxNyS/NUtRYM4JF3HsQAOgQDcEHyMYlsTsgtzVPVWjCAZneJ9tYA5nertF8d926N6myGcrj1eDEPPu8Cmr85DLc8q9a0NpoevXd3GK7bo982GxyiMjAAFyQfk8jmhNzSPFWtnw3AjONm4t6ttnwmC/Sy8+AzA7BGf7MNIH8rawWbzsIvKzOADkM4AcBjXvhUEFzyocN68t8Ewdu+u2GNHQ88+8L1QfDW/Wrxt39Jr2nFw8cbbwiCX9mv1/3u9cElH+60ms4MYDr8QPwzf35Shfvmbfn5STU5r3p/innwxgDieVuOR0/r8Z8LBqDWVGNGJyuE5eGgO/umGAAA9JqfbNDDOK/JJi8tGoCZueL+2dmHzYDPGy0DSDbWafHJ0g7L6cwAEmZqDGBPssTOgkkN4Oz4FWYs0LIBqIj4uXiuvUJl0AXkguSzcmRzQm5pfqg2FbxLp7tsU1kw8eRjY4Wgl3jm5mjhS/HicxMrD0TRaTsbrJgwM6KCgXudB7D43IN33xwWDUD35lx+6/NRIQ8+M4A507CfrbkGkE7OaSuo+CoxBuCC5GMS2ZyQW5oXqiU9/gs7RtLJUwUDyBMjY378wKevDBqFQyZ5kj3NBF7cncS3Fw1g0SS6rPuWnQdvGUDevmMAurbq37IiJB+TyOaE3NK8UC1J9wqCLOxdNfl21OPm0qpLxMP31ACmw9GP3fPg86UuIMVzd18T6u78LA+eM4DGtVX/lhUh+ZhENifkluaFapkBrDy4hAGcmwiuuuErj08INoAkwGtxlzaAPNvdcDZeuuQ1gHyj3AC4BiAGyccksjkhtzQvVCv8wre6gJIOHasL6FSwLTLz6ncBiTAAdbV3frvuAsqz3c+Or1P3/T8dagOw8uCT20Cnw/VpCrwVCJ8bgH2bUGVgAC5IPiaRzQm5pXmh2sKO4RvjlvtR1fhPZReB48nN6rquvgh8UxTPHFHGcEwlBOvM+LtqLwILMID03n19w6Z9d392S7+dB1/vOYD6jwSkjxdgAL1G8jGJbE7ILc0P1ZI+oLhJTyavUl5wSk1dtjVu1M1MlQk/kfYVRUeDYES3+PnGMgxg8dC4ut/nWd1nk2e76yeBw/c/VMyDt54E3h2GW8ya+Ub2NQR1UrHuv1R8DYDhoNtH8jGJbE7ILc0T1c7sjJvwTQfSyat/pHuCHhkLNj2pG/X4R39wmVqsJt50k/r1fy4+D0gWxlsMX/1EJMQAukpyLbgiPNm7KkayasjmhNzSUM0F2YPBOXF2fO1D+izg9grflL3LBcmqIZsTcktDNRcG0AAWd1V/CYC9ywnJqiGbE3JLQzUXBtAAkksLt1X6nuxdLkhWDdmckFsaqrkwiAYAAADLDgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKcMqAEs7h43GcWwJOcn0yCegmpI2IRnbg7D0bpKIVtjntkeq3brcfMC1drgrA5hj7qg2mAawPlJnTCm8uhhKabTJLaCakjYhEMmwW70M+oFsrXIjFFtXa1QqNacxV2hMYDqVRtMA5gOr3gomk9VgyYsTmdRnAXVkLAxc+HobZESRx97yNYaZ8e1atvN7oZqbRBbp5GmetUG0gDOjusj8/yk+YkGjVFn5YkBFFRDwsbEv8duV/+Pf4HdjmwtMx1eq/5nJEK1Njg7nhhAF1QbSAOYSZq0GbPLQUPinxZbns7UslRDwsacn0zOuqdrlEK2JTHqoVrrxD84/tBcA+iCagNpANPmF1p8qr5+iTV9Z2bdnkylgmpI2ALaAJCtPcz1TFRrnelwfXIRuAuqDaIBLO5KToqya+fQhGQHKqiGhC2gT76RrT2eHlftFqq1zlx4xXGjTDdUwwC8BwNwRJ+DI1s7TIfh6J4I1dpA/8zAANrBkoYby5am1gDWfgsJl2ZO3waKbG2w+B/+p/Fw9I9QrQ10P2ONAVSm2oAbAL8oloYzACfmxkdVHyyytckzqg8I1VplRt//wxlAO7BDtQUG4MJM8hgYsrWL6tNGtRY5O661wQDagrsK2oG7gBw4FKZ3XyNbm+gGC9VaI3l6Onnil7uAWiO9M5b7ilthrnQ/cXKTMRI2ZnE6GdAgQrZWSR+fMwaAaq1RNIAuqDaQBsCThe2QGgAPZ7bMtPX4PbK1yHR2prke1dok6ebhSeDWiH9srGNskVZJDaCgGhI2YcZWBdla5Ox4uOV4tHgouXkK1dogMYAuqDaQBhDNM7pg62R9iAXVkLAhySiMYTKKErK1yFwyhqruCUK1dkgv9Fav2mAaQDS/O1bm/fyeaIX8IlJBNSRsxFzeL6uVQ7YWmbdTFFCtDbI7fSpXbUANAAAAlgIDAADwFAwAAMBTMAAAAE/BAAAAPAUDAADwFAwAAMBTMAAAAE/BAAAAPAUDAADwFAwAAMBTMAAAAE/BAAAAPAUDAADwFAwA4MiQzYW9LgdgucAAADAA8BQMAAADAE/BAACODL3qC72uAaAHYAAAGAB4CgYAgAGAp2AAADUG8MrHh977g/cMDb36j+MXL33ioqGhC97w+XShWnDBb/7QbPSzdw9dXHqTwvpq+St/+br4rX7zh8n2evnPf1JN/vSiZOt4NS49wPKDAQDUM4BfV9eD1ezsCvEb00Wa136ugQEU14+X/4v3mNev+UJkL3+jWfpa7QuxE7x3eb4rgAUGAGDfBaRbcdXKX/DH0Uuf1MvUj/l//mzSou+N///D+Fd8smqNAZTWj5cPXfBb8QbxO16cfNZrPx+98lnzcm/mGnRCQQ/AAADqGoD5RR7/NL8wXSm2hLzTZm99AyivrwxAv1P8lurHfvqbP36pVj9plsav6AGCHoABANQzgFelHTbpT3PTSKc/2VVLXs8AyutnnTzJlidTZzmZ+oNq+ekBgt6AAQDUuwZgmm37p/neeJ71em89Ayivb13dLRuE/S70AEFvwAAA6hmAabZVD459dmDdrVP3LqDy+jVnCHvTE4IEfUZADxD0CAwAoC0DuLiwUacGoJfTAwQ9AgMAaGoAhd/mLZwBFH/LL2UAesZJeoCgN2AAAI0NILsYUF5Qew1gb3INoM4vfOtDsovI6Tupi8F76QGC3oABADQ2gLjBVjfrJPPitv2I1YAnBnCh/bq8ftkATlr3l+qJeIVfeDc9QNAbMACAJgYQt9PJT3rTdGf3+R8xd4xmP/lPDqXPARTWLxtAdltodiqwd+jVF9EDBL0BAwBoYgCqoX/NJ+M5nxsyLbd50lc9yZs9+aue7P3Li/LX9vo1TwqfLDwJbGbQAwQ9AgMAaGYA+VNiyS/75PUF/2P+0LA1NlB5/cZjBb0xef/sWWGAZQcDAGhqANFLn3jdUDp8p3mtB/tMNzJjff62PRqotX6j0ULzt8s7gwCWGwwAwInKnt49Qg8Q9AoMAMCJqgxAZQ9U8T4A7YMBADhRlQH8lHuAoGdgAABOVGQAL308G1wCYLnBAACcqMQAjqR3kwL0AgwAwIlKDODk0NBrPr/0agDdAQMAAPAUDAAAwFMwAAAAT8EAAAA8BQMAAPAUDAAAwFMwAAAAT8EAAAA8BQMAAPAUDAAAwFMwAAAAT8EAAAA8BQMAAPAUDAAAwFMwAAAAT8EAAAA8BQMAAPAUDAAAwFMwAAAAT8EAAAA8BQMAAPAUDAAAwFMwAAAAT8EAAAA8BQMAAPAUDAAAwFMwAAAAT8EAAAA8BQMAAPAUDAAAwFMwAAAAT8EAAAA8BQMAAPAUDAAAwFMwAAAAT8EAAAA8BQMAAPAUDAAAwFMwAAAAT8EAAAA8BQMAAPAUDAAAwFMwAAAAT8EAoDlDbdHrat3or+/YV8WCbNhBoDlDP9cGfbo7Df16G/T8Ow7959bpebEgG3YQaA4GgAHAwMIOAs3BADAAGFjYQaA5GAAGAAMLOwg0BwPAAGBgYQeB5mAAGAAMLOwg0BwMAAOAgYUdBJqDAWAAMLCwg0BzMAAMAAYWdhBoDgaAAcDAwg4CzcEAMAAYWNhBoDkYAAYAAws7CDQHA8AAYGBhB4HmYAAYAAws7CDQHAwAA4CBhR0EmtN9AzgydHHVRbf5EZUbwMmhC1us7JW/fOPS9ZWq7boBtFzOz979qi8kX6GlVUEaGAA0BwPoqgGYNfvaAFr7shiASDAAaA5dQMtgAO1VK6cLCAPod/r0iIVlAwPAABqCAfQ7fXrEwrLRmQG89GtDQ0Ov/+3kxSfiF2/4vHnxg/cMDb1aL0g6HApLX/rERUNDP1+zamGtvUPv/f7rsk2KC376nqELfiuK/utFQ6/+ZP4R9hsVvmNnBlAo9vsXDV3w27pN3Gs+9IhpH+3P/oFS5dW/+cPolY+r3N4L60hQ/HLFajsyANd/kXyp9b3iVv3/Sr6C/W1PDl0cy/CaP7a+KQYgEwwAmtORAfz0IhNNrpvAk+bFBe9VL46YBRdHaXNTWPrTBqsW1to79Hr9QjcsxQW/oV9dvFfP+uP0I+w3Kn7HjgygUKz+yKFfrzEA+7M/O5SqUjSAhl+uVG0nBuD6L2ItbcUA/rt429f+0PqmGIBMMABoTicGELdvvxD/+PvBRaoRjpueN/xD9Mpnh5IX8ZLv6/ZNNzeFpT9799Ab45/Hn9OL7VULa8Vt7YU/VL+431u74I0/jLeOf4pHL31ctT7pR2RvVPqOnRhAodiT6jNf2TtUNgD7s+N11FnJ93Wt1kXghl+uXG0HBuD6L2IvLRpA2gVUNADV+he/KQYgEgwAmtOJAfzs3arxSEi6QuKW4sLsxV7Vvunmpt7SwpJkVWutvaaV2VuzuVkQt1n6d6x6UfNGpe/YiQEUijXN4CsfLxuA/dnJ/Hil9xYMoOGXK1fbgQF0+C9i19/UAMyn2N8UAxAJBgDN6fAM4DV/lU+bhle1yNkLhWpuSkuTVuqnFxVXLaxlWibT5JQXqHYnaesyAyh8Zuk7dmAAhWLT9vVIyQBqPvuf/9//5T1DRQNo+OVqqu3sDMDtX8Ra2ooBaPcqfFMMQCQYADSno2sAuuf4Nf9OtQbZb0/VEpR+iF5cXGp6xpMucHvVwlrFhqh2QdkACp9Z+o4dGUChWNPKlS8CFz/7pfckGxQMoOGXq6m2AwNw/BcpLG3DAKxvigGIBAOA5nR2F5C6k2VIdcnnDYBpUKzWwDQ3haV12tR0YT5RboisBe+N6hlAwxaoEwMoFPvTixoZgPXZ6jrsBT//G3/18bIBNPhyNdV2YgCu/yLW0lYM4MKab4oBiAQDgOZ0+hzAK//rr+m7R1x/bzqdAdQ3gK6cAZSKXfoMQF0g+GFUew1gec4Aos7/RVo2gMI3xQBEggFAcyp4EOyVz9ndyG32ODe7BlDbx54sqGcAXbsGYL1tK9cA0nXiU4em1wC6ZgBRh/8iLRtA4ZtiACLBAKA5nRhA2iOiro/WvavkSHaPZmmp6UNOGm971cKNMjV32aQL6hlA4Y1K37EDAygWa98FZD7Ini6cDZzUd9U3vguoKwbg/i9S/kfQ38syAPvblgzg5FDzHjjoHRgANKfDu4Be+/kouRff3Ff+z5+w7iv/wUXZTYeFpfFPRrVddot/tmphrdJ99vaCugZgv1HpO3ZiAOViL1b3zid3w+vnEPLnAPRnm44R9ZiCMQBlH7USdMkAXP9F7KXW90oMQH0F+9sWuoCSb4oBiAQDgOZU8iTwa8oP66ZPluZDYRaWJi+G3lhatfSwrNVGFhfUNYDCGxW/YycGUK9Y/SSwuT/oVf/b0IXFzz5pJl/7uaQRjgus9yRwd7qAXP9FrKXW99KtuvkK9rdNLwLb3xQDEAkGAM3pcCwgNUyOHgsmchgL6JPlVUvD5dhtZHEcnaieAXR3LKCk2O+/Lh0LKHolLuk1n09aQ+uzk0nzy/m/XpQZQOMvV6y2EwNw/hex6s+/l2nV9Vewv202PJz1TTEAkWAA0BxGA13SAJa72o4MAMCCHQSagwFgADCwsINAczAADAAGFnYQaA4GgAHAwMIOAs3BADAAGFjYQaA5GAAGAAMLOwg0BwPAAGBgYQeB5mAAGAAMLOwg0BwMAAOAgYUdBJqDAWAAMLCwg0BzMAAMAAYWdhBoDgaAAcDAwg4CzRlqi15X60Z/fce+KhZkww4CAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAOApGAAAgKdgAAAAnoIBAAB4CgYAAPD/t1cHAgAAAACC/K0HuSSaEgDAlAAApgQAMCUAgCkBAEwJAGBKAABTAgCYEgDAlAAApgQAMCUAgCkBAEwJAGBKAABTAgCYEgDAlAAApgQAMCUAgCkBAEwJAGBKAABTAgCYEgDAlAAApgQAMCUAgCkBAEwJAGBKAABTAgCYEgDAlAAApgQAMCUAgCkBAEwJAGBKAABTAgCYEgDAlAAApgQAMCUAgCkBAEwJAGBKAABTAgCYEgDAlAAApgQAMCUAgCkBAEwJAGBKAABTAgCYEgDAlAAApgQAMCUAgCkBAEwJAGBKAABTAgCYEgDAlAAApgQAMCUAgCkBAEwJAGBKAABTAgCYEgDAlAAApgQAMCUAgKkAa7KS84WCPv0AAAAASUVORK5CYII=" width="768" /></p>
</div>
<div id="figure-c1" class="section level2">
<h2>Figure C1</h2>
<pre class="r"><code>medium_dim &lt;-  textstat_frequency(dfmat_med, n = 20)

translations_med &lt;- c(&quot;job&quot;, &quot;no idea&quot;, &quot;satisfied&quot;, &quot;diligent&quot;, &quot;family&quot;, &quot;to afford&quot;, &quot;civil servants&quot;, &quot;work&quot;, &quot;working population&quot;, &quot;families&quot;, &quot;money&quot;, &quot;income&quot;, &quot;normal&quot;, &quot;middle class&quot;, &quot;middle class&quot;, &quot;craftsmen&quot;, &quot;to work&quot;, &quot;the normal&quot;, &quot;employees&quot;, &quot;workers&quot;)

medium_dim &lt;- dict_df %&gt;% 
  left_join(medium_dim, ., by = c(&quot;feature&quot;))

plot_med_f &lt;- medium_dim %&gt;% 
  ggplot(aes(x = reorder(feature, frequency), y = frequency, fill = dimension)) +
  geom_col(width = 0.7, size = 0.2, color = &quot;black&quot;) +
  coord_flip() +
  scale_fill_manual(values = c(&quot;gray1&quot;, 
                               &quot;gray40&quot;, 
                               &quot;gray72&quot;),
                    na.value = &quot;gray99&quot;,
                    name = NULL,
                    na.translate = F) +
  annotate(geom = &quot;text&quot;, x = 1:20, y = 280, size = 2.5, color = &quot;gray25&quot;, 
           label = translations_med, hjust = 1)+
  labs(x = NULL, y = &quot;Frequency&quot;, subtitle = &quot;Static: medium&quot;) +
  theme(legend.position = &quot;bottom&quot;,
         axis.text.y = element_text(size = 8)) 

plot_med_f</code></pre>
<p><img role="img" 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" width="672" /></p>
</div>
<div id="table-c1" class="section level2">
<h2>Table C1</h2>
<pre class="r"><code>df_dyn_stat %&gt;% 
  group_by(Question) %&gt;% 
  summarise( 
          &quot;Socioeconomic.N&quot; = sum(eco == 1),
          &quot;Socioeconomic.%&quot; = mean(eco) * 100,
          &quot;Sociocultural.N&quot; = sum(cult == 1),
          &quot;Sociocultural.%&quot; = mean(cult) * 100,
          &quot;Education.N&quot; = sum(edu == 1),
          &quot;Education.%&quot; = mean(edu) * 100,
          &quot;Politics.N&quot; = sum(pol == 1),
          &quot;Politics.%&quot; = mean(pol) * 100,
          &quot;Attributes.N&quot; = sum(attr == 1),
          &quot;Attributes.%&quot; = mean(attr) * 100,
          &quot;Dontknow.N&quot; = sum(dk == 1),
          &quot;Dontknow.%&quot; = mean(dk) * 100,
          &quot;Total.N&quot; = n(),
          &quot;Total.%&quot; = mean(eco + cult + edu + pol + attr) * 100
          ) %&gt;% 
  ungroup() %&gt;% 
  pivot_longer(cols  = -Question, names_to = c(&quot;Dimension&quot;, &quot;.value&quot;), 
    names_sep = &quot;\\.&quot;) %&gt;% 
  arrange(fct_relevel(factor(Dimension), 
                            &quot;Socioeconomic&quot;, &quot;Sociocultural&quot;, &quot;Education&quot;,
                            &quot;Politics&quot;, &quot;Attributes&quot;, &quot;Dontknow&quot;, &quot;Total&quot;),
          desc(Question)) %&gt;% 
  relocate(Dimension) %&gt;% 
  kbl(caption = &quot;Salience of dictionary dimensions (percentage of answers mentioning the dimension by question type)&quot;,
      booktabs = T,
      digits = 1,
      #format = &quot;latex&quot;
      ) %&gt;% 
  collapse_rows(columns = 1:2, latex_hline = &quot;major&quot;) %&gt;% 
  kable_styling()</code></pre>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>
Salience of dictionary dimensions (percentage of answers mentioning the
dimension by question type)
</caption>
<thead>
<tr>
<th style="text-align:left;">
Dimension
</th>
<th style="text-align:left;">
Question
</th>
<th style="text-align:right;">
N
</th>
<th style="text-align:right;">
%
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;vertical-align: middle !important;" rowspan="2">
Socioeconomic
</td>
<td style="text-align:left;">
static
</td>
<td style="text-align:right;">
5183
</td>
<td style="text-align:right;">
47.2
</td>
</tr>
<tr>
<td style="text-align:left;">
dynamic
</td>
<td style="text-align:right;">
2820
</td>
<td style="text-align:right;">
38.4
</td>
</tr>
<tr>
<td style="text-align:left;vertical-align: middle !important;" rowspan="2">
Sociocultural
</td>
<td style="text-align:left;">
static
</td>
<td style="text-align:right;">
529
</td>
<td style="text-align:right;">
4.8
</td>
</tr>
<tr>
<td style="text-align:left;">
dynamic
</td>
<td style="text-align:right;">
847
</td>
<td style="text-align:right;">
11.5
</td>
</tr>
<tr>
<td style="text-align:left;vertical-align: middle !important;" rowspan="2">
Education
</td>
<td style="text-align:left;">
static
</td>
<td style="text-align:right;">
940
</td>
<td style="text-align:right;">
8.6
</td>
</tr>
<tr>
<td style="text-align:left;">
dynamic
</td>
<td style="text-align:right;">
647
</td>
<td style="text-align:right;">
8.8
</td>
</tr>
<tr>
<td style="text-align:left;vertical-align: middle !important;" rowspan="2">
Politics
</td>
<td style="text-align:left;">
static
</td>
<td style="text-align:right;">
787
</td>
<td style="text-align:right;">
7.2
</td>
</tr>
<tr>
<td style="text-align:left;">
dynamic
</td>
<td style="text-align:right;">
759
</td>
<td style="text-align:right;">
10.3
</td>
</tr>
<tr>
<td style="text-align:left;vertical-align: middle !important;" rowspan="2">
Attributes
</td>
<td style="text-align:left;">
static
</td>
<td style="text-align:right;">
2820
</td>
<td style="text-align:right;">
25.7
</td>
</tr>
<tr>
<td style="text-align:left;">
dynamic
</td>
<td style="text-align:right;">
1379
</td>
<td style="text-align:right;">
18.8
</td>
</tr>
<tr>
<td style="text-align:left;vertical-align: middle !important;" rowspan="2">
Dontknow
</td>
<td style="text-align:left;">
static
</td>
<td style="text-align:right;">
352
</td>
<td style="text-align:right;">
3.2
</td>
</tr>
<tr>
<td style="text-align:left;">
dynamic
</td>
<td style="text-align:right;">
322
</td>
<td style="text-align:right;">
4.4
</td>
</tr>
<tr>
<td style="text-align:left;vertical-align: middle !important;" rowspan="2">
Total
</td>
<td style="text-align:left;">
static
</td>
<td style="text-align:right;">
10987
</td>
<td style="text-align:right;">
93.4
</td>
</tr>
<tr>
<td style="text-align:left;">
dynamic
</td>
<td style="text-align:right;">
7349
</td>
<td style="text-align:right;">
87.8
</td>
</tr>
</tbody>
</table>
</div>
<div id="table-c2" class="section level2">
<h2>Table C2</h2>
<pre class="r"><code>df_subd &lt;- df_dyn_stat %&gt;%
  group_by(Question) %&gt;% 
  rowwise() %&gt;% 
  mutate(
    sum_ecoocc = sum(c_across(starts_with(&quot;socioeconomic.occ&quot;)), na.rm = T), # sum per answer,
    sum_ecowealth = sum(c_across(starts_with(&quot;socioeconomic.wealth&quot;)), na.rm = T), 
    sum_cultgen = sum(c_across(starts_with(&quot;sociocultural.gender&quot;)), na.rm = T), # sum per answer,
    sum_cultsex = sum(c_across(starts_with(&quot;sociocultural.sexuality&quot;)), na.rm = T), # sum per answer,
    sum_cultrace = sum(c_across(starts_with(&quot;sociocultural.race&quot;)), na.rm = T), # sum per answer,
    sum_edu = sum(c_across(starts_with(&quot;education&quot;)), na.rm = T), # sum per answer
    ecoocc = if_else(sum_ecoocc &gt; 0, 1, 0), # binary: 1 is eco. mentioned, 0 not
    ecowealth = if_else(sum_ecowealth &gt; 0, 1, 0), # binary: 1 is eco. mentioned, 0 not
    cultgen = if_else(sum_cultgen &gt; 0, 1, 0), # binary: 1 is cult. mentioned, 0 not
    cultsex = if_else(sum_cultsex &gt; 0, 1, 0), # binary: 1 is cult. mentioned, 0 not
    cultrace = if_else(sum_cultrace &gt; 0, 1, 0), # binary: 1 is cult. mentioned, 0 not
    edu = if_else(sum_edu &gt; 0, 1, 0), # binary: 1 is edu. mentioned, 0 not

    ) %&gt;% 
  ungroup()


df_subd %&gt;% 
  group_by(Question) %&gt;% 
  summarise( 
          &quot;Socioeconomic.Occupation.N&quot; = sum(ecoocc == 1),
          &quot;Socioeconomic.Occupation.pct&quot; = mean(ecoocc) * 100,
          &quot;Socioeconomic.Wealth.N&quot; = sum(ecowealth == 1),
          &quot;Socioeconomic.Wealth.pct&quot; = mean(ecowealth) * 100,
          &quot;Sociocultural.Gender.N&quot; = sum(cultgen == 1),
          &quot;Sociocultural.Gender.pct&quot; = mean(cultgen) * 100,
          &quot;Sociocultural.Sexuality.N&quot; = sum(cultsex == 1),
          &quot;Sociocultural.Sexuality.pct&quot; = mean(cultsex) * 100,
          &quot;Sociocultural.Race.N&quot; = sum(cultrace == 1),
          &quot;Sociocultural.Race.pct&quot; = mean(cultrace) * 100,
          &quot;Education.Education.N&quot; = sum(edu == 1),
          &quot;Education.Education.pct&quot; = mean(edu) * 100,
          &quot;Total.Total.N&quot; = n(),
          &quot;Total.Total.pct&quot; = 100,
          ) %&gt;% 
  ungroup() %&gt;% 
  pivot_longer(cols  = -Question, names_to = c(&quot;Dimension&quot;, &quot;Subdimension&quot;, &quot;.value&quot;), 
    names_sep = &quot;\\.&quot;) %&gt;% 
  arrange(desc(Question), fct_relevel(factor(Dimension), 
                            &quot;Socioeconomic&quot;, &quot;Sociocultural&quot;, &quot;Education&quot;,
                           &quot;Total&quot;
                      )) %&gt;% 
  kbl(caption = &quot;Salience of dictionary subdimensions by question type (percentage of answers mentioning the dimension)&quot;,
      booktabs = T,
      digits = 1,
      #format = &quot;latex&quot;
      ) %&gt;% 
  collapse_rows(columns = 1:2, latex_hline = &quot;major&quot;) %&gt;% 
  kable_styling()</code></pre>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>
Salience of dictionary subdimensions by question type (percentage of
answers mentioning the dimension)
</caption>
<thead>
<tr>
<th style="text-align:left;">
Question
</th>
<th style="text-align:left;">
Dimension
</th>
<th style="text-align:left;">
Subdimension
</th>
<th style="text-align:right;">
N
</th>
<th style="text-align:right;">
pct
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;vertical-align: middle !important;" rowspan="7">
static
</td>
<td style="text-align:left;vertical-align: middle !important;" rowspan="2">
Socioeconomic
</td>
<td style="text-align:left;">
Occupation
</td>
<td style="text-align:right;">
2996
</td>
<td style="text-align:right;">
27.3
</td>
</tr>
<tr>
<td style="text-align:left;">
Wealth
</td>
<td style="text-align:right;">
3005
</td>
<td style="text-align:right;">
27.4
</td>
</tr>
<tr>
<td style="text-align:left;vertical-align: middle !important;" rowspan="3">
Sociocultural
</td>
<td style="text-align:left;">
Gender
</td>
<td style="text-align:right;">
73
</td>
<td style="text-align:right;">
0.7
</td>
</tr>
<tr>
<td style="text-align:left;">
Sexuality
</td>
<td style="text-align:right;">
14
</td>
<td style="text-align:right;">
0.1
</td>
</tr>
<tr>
<td style="text-align:left;">
Race
</td>
<td style="text-align:right;">
434
</td>
<td style="text-align:right;">
4.0
</td>
</tr>
<tr>
<td style="text-align:left;">
Education
</td>
<td style="text-align:left;">
Education
</td>
<td style="text-align:right;">
940
</td>
<td style="text-align:right;">
8.6
</td>
</tr>
<tr>
<td style="text-align:left;">
Total
</td>
<td style="text-align:left;">
Total
</td>
<td style="text-align:right;">
10987
</td>
<td style="text-align:right;">
100.0
</td>
</tr>
<tr>
<td style="text-align:left;vertical-align: middle !important;" rowspan="7">
dynamic
</td>
<td style="text-align:left;vertical-align: middle !important;" rowspan="2">
Socioeconomic
</td>
<td style="text-align:left;">
Occupation
</td>
<td style="text-align:right;">
2221
</td>
<td style="text-align:right;">
30.2
</td>
</tr>
<tr>
<td style="text-align:left;">
Wealth
</td>
<td style="text-align:right;">
868
</td>
<td style="text-align:right;">
11.8
</td>
</tr>
<tr>
<td style="text-align:left;vertical-align: middle !important;" rowspan="3">
Sociocultural
</td>
<td style="text-align:left;">
Gender
</td>
<td style="text-align:right;">
390
</td>
<td style="text-align:right;">
5.3
</td>
</tr>
<tr>
<td style="text-align:left;">
Sexuality
</td>
<td style="text-align:right;">
175
</td>
<td style="text-align:right;">
2.4
</td>
</tr>
<tr>
<td style="text-align:left;">
Race
</td>
<td style="text-align:right;">
406
</td>
<td style="text-align:right;">
5.5
</td>
</tr>
<tr>
<td style="text-align:left;">
Education
</td>
<td style="text-align:left;">
Education
</td>
<td style="text-align:right;">
647
</td>
<td style="text-align:right;">
8.8
</td>
</tr>
<tr>
<td style="text-align:left;">
Total
</td>
<td style="text-align:left;">
Total
</td>
<td style="text-align:right;">
7349
</td>
<td style="text-align:right;">
100.0
</td>
</tr>
</tbody>
</table>
</div>
<div id="figure-c2" class="section level2">
<h2>Figure C2</h2>
<pre class="r"><code>model_soceconl3 &lt;- glm(socioeconomic ~ galtan_group + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_soceconw3 &lt;- glm(socioeconomic ~ galtan_group + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)

pred_sel &lt;- avg_predictions(model_soceconl3, by = &quot;galtan_group&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as losing groups&quot;)

pred_sew &lt;- avg_predictions(model_soceconw3, by = &quot;galtan_group&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as winning groups&quot;)

pred_econ &lt;- bind_rows(pred_sel, pred_sew) %&gt;% 
    mutate(question = fct_rev(factor(question)))

ggplot(pred_econ, aes(question, estimate, color = galtan_group)) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high),
                  position = position_dodge(width = 0.5)) +
  scale_color_grey(start = 0.1, end = 0.7, name = &quot;Subgroup&quot;)+
  theme(legend.position = &quot;bottom&quot;)+
  labs(y = &quot;Predicted probability of mentioning groups&quot;, x = NULL
       )</code></pre>
<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
</div>
<div id="figure-c3" class="section level2">
<h2>Figure C3</h2>
<pre class="r"><code>model_soccultl2 &lt;- glm(sociocultural ~ galtan_att + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_soccultl2b &lt;- glm(sociocultural ~ galtan_att + I(galtan_att^2) + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_soccultw2 &lt;- glm(sociocultural ~ galtan_att + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)

model_soccultw2b &lt;- glm(sociocultural ~ galtan_att + I(galtan_att^2) + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)

p1 &lt;- plot_predictions(model_soccultw2, condition = c(&quot;galtan_att&quot;)) + 
  labs(subtitle = &quot;Continuous GAL-TAN: Winning groups&quot;, 
       x = &quot;GAL-TAN attitudes&quot;, y = &quot;Pred. probability sociocultural mention&quot;) +
  scale_y_continuous(limits = c(0.04, 0.27))

p2 &lt;- plot_predictions(model_soccultl2, condition = c(&quot;galtan_att&quot;)) + 
  labs(subtitle = &quot;Continuous GAL-TAN: Losing groups&quot;,  
       x = &quot;GAL-TAN attitudes&quot;, y = &quot;Pred. probability sociocultural mention&quot;) +
  theme(axis.title.y = element_blank()) +
  scale_y_continuous(limits = c(0.04, 0.27))

p3 &lt;- plot_predictions(model_soccultw2b, condition = c(&quot;galtan_att&quot;)) + 
  labs(subtitle = &quot;Polynomial: Winning groups&quot;,
       x = &quot;GAL-TAN attitudes&quot;, y = &quot;Pred. probability sociocultural mention&quot;)+
  scale_y_continuous(limits = c(0.04, 0.27))

p4 &lt;- plot_predictions(model_soccultl2b, condition = c(&quot;galtan_att&quot;)) + 
  labs(subtitle = &quot;Polynomial: Losing groups&quot;,
       x = &quot;GAL-TAN attitudes&quot;, y = &quot;Pred. probability sociocultural mention&quot;) +
  theme(axis.title.y = element_blank())+
  scale_y_continuous(limits = c(0.04, 0.27))

plots_galtan &lt;- arrangeGrob(p1, p2, p3, p4,
                          ncol = 2, nrow = 2,
                          widths = unit(c(10, 9.5), c(&quot;cm&quot;)))

grid.arrange(plots_galtan)</code></pre>
<p><img role="img" 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JyhYAHrwdUuEbDvAcCCCIAsAFhgnKFgAevB1S4RsO8BwIIIgCwAWGCcoWAB68HVLhGw7wHAggiALABYYJyhYAHrwdUuEbDvAcCCCIAsAFhgnKFgAevB1S4RsO8BwIIIgCwAWGCcoWAB68HVLhGw7wHAggiALABYYJyhYAHrwdUuEbDvAcCCCIAsAFhgnKFgAevB1S4RsO8BwIIIgCwAWGCcoWAB68HVLhGw7wHAggiALABYYJyhYAHrwdUuEbDvAcCCCIAsAFhgnKFgAevB1S4RsO8BwIIIgCwAWGCcoWAB68HVLhGw7wHAggiALABYYJyhYAHrwdUuEbDvAcCCCIAsAFhgnKFgAevB1S4RsO8BwIIIgCwAWGCcoWAB68HVLhGw7wHAggiALABYYJyhYAHrwdUuEbDvAcCCCIAsAFhgnKFgAevB1S4RsO8BwIIIgCwAWGCcoWAB68HVLhGw7wHAggiALABYYJyhYAHrwdUuEbDvAcCCCIAsAFhgnKFgAevB1S4RsO8BwIIIgCwAWGCcoWAB68HVLhGw7wHAggiALABYYJyhYAHrwdUuEbDvAcCCCIAsAFhgnKFgAevB1S4RsO8BwIIIgCwAWGCcoWAB68HVLhGw7wHAggiALABYYJyhYAHrwdUuEbDvAcCCCIAsAFhgnKFgAevB1S4RsO8BwIIIgCwAWGCcoWAB68HVLhGw7wHAggiALABYYJyhYAHrwdUuEbDvAcCCCIAsAFhgnKFgAevB1S4RsO8BwIIIgCwAWGCcoWAB68HVLhGw7wHAggiALABYYJyhYAHrwdUuEbDvAcCCCIAsAFhgnKFgAevB1S4RsO8BwIIIgCwAWGCcoWAB68HVLhGw7wHAggiALABYYJyhYAHrwdUuEbDvAcCCCIAsAFhgnKFgAevB1S4RsO8BwIIIgCwAWGCcoWAB68HVLhGw7wHAggiALABYYJyhYAHrwdUuEbDvAcCCCIAsAFhgnKFgAevB1S4RsO8BwIIIgCwAWGCcoQi4gL/OeaUkwNUuEbDvAcCCCIAsAFhgnKEItIDfPIzWXHquI8LVLhGw7wHAggiALABYYJyhCLOAL44iFrBMQl3AvgcACyIAsgBggXGGIswCnkfR/vuf5nz2WkeFq10iYN8DgAURAFkAsMA4QxFkAS+Oo6sjyHC1SwTsewCwIAIgCwAWGGcogizgi6O9j0aQ4WqXCNj3AGBBBEAWACwwzlAEWsBaH/tW4GqXCNj3AGBBBEAWACwwzlAEWcCLY74ClkqoC9j3AGBBBEAWACwwzlAEWcDLeXTLzYa+JWRy3GSZeSY+4C7NFgizgFcvge/rq/Cf2xIB+x4ALIgAyAKABcYZiiALePHoThTtXb+b8R4PQ2qXUBew7wHAggiALABYYJyhCLKAq+fh4Ik4uiTUBex7ALAgAiALABYYZyhYwCzgTgl1AfseACyIAMgCgAXGGYogC3gkuNolAvY9AFgQAZAFAAuMMxQsYD242iUC9j0AWBABkAUAC4wzFCxgPbjaJQL2PQBYEAGQBQALjDMUwRbw26cHUbR3cE/rYsBLrnaZgH0PABZEAGQBwALjDEWoBTwvdsFydEqOBrjaJQL2PQBYEAGQBQALjDMUgRZw3L+Xr9+9c02zgbnaJQL2PQBYEAGQBQALjDMUYRbw+Sy68iz525vjSO280FztEgH7HgAsiADIAoAFxhmKMAv4JLqSn/1K8drAXO0SAfseACyIAMgCgAXGGYogC7hyNaTz2RWeirJdQl3AvgcACyIAsgBggXGGIsgCrlwPWO/iwFztEgH7HgAsiADIAoAFxhkKFjALuFNCXcC+BwALIgCyAGCBcYYiyAJeHEcPii/OIr4F3SGhLmDfA4AFEQBZALDAOEMRZAFzJyy5hLqAfQ8AFkQAZAHAAuMMRZgFfD6L9j9P/vbVbR6G1CmhLmDfA4AFEQBZALDAOEMRZgGnJ8I6ODhQPRUWV7tEwL4HAAsiALIAYIFxhiLQAl6+mGVnoty7r6bB1S4RsO8BwIIIgCwAWGCcoQi1gJeLl3dWr4CvP1baASuGq10iYN8DgAURAFkAsMA4QxFsAY8AV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGIrACXjy6+97r+P9l3uNxwO0S6gL2PQBYEAGQBQALjDMUgRXwxVF06Xn8/zI8E1aHhLqAfQ8AFkQAZAHAAuMMBQuYBdwpoS5g3wOABREAWQCwwDhDEVgBjwpXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYYiyAJePCrtd3V+5yfcCatdQl3AvgcACyIAsgBggXGGIsgC5uUI5RLqAvY9AFgQAZAFAAuMMxQs4PMZC7hDQl3AvgcACyIAsgBggXGGIrQCru0AncDrAXdIqAvY9wBgQQRAFgAsMM5QhFbAy7PNAn6gJMXVLhGw7wHAggiALABYYJyhCK6AF/9y9+6d2d714jxY73+uJcXVLhGw7wHAggiALABYYJyhCK6AY/T2u6rA1S4RsO8BwIIIgCwAWGCcoQiygCuHIenB1S4RsO8BwIIIgCwAWGCcoQiygEeCq10iYN8DgAURAFkAsMA4QxFwAX+d80pJgKtdImDfA4AFEQBZALDAOEMRaAG/eciLMcgk1AXsewCwIAIgCwAWGGcowizg6tHALOAOCXUB+x4ALIgAyAKABcYZijALeB5F++9/mvMZT8TRLqEuYN8DgAURAFkAsMA4QxFkAS+Oo6sjyHC1SwTsewCwIAIgCwAWGGcogizgi6O9j0aQ4WqXCNj3AGBBBEAWACwwzlAEWsA8EYdUQl3AvgcACyIAsgBggXGGIsgCXhzzFbBUQl3AvgcACyIAsgBggXGGIsgCXs6jWyOocLVLBOx7ALAgAiALABYYZyjCLODVS+D7+ipc7RIB+x4ALIgAyAKABcYZiiALePHoThStL4ikdWJornaJgH0PABZEAGQBwALjDEWQBVw9DwdPxNEloS5g3wOABREAWQCwwDhDwQJmAXdKqAvY9wBgQQRAFgAsMM5QBFnAI8HVLhGw7wHAggiALABYYJyhYAHrwdUuEbDvAcCCCIAsAFhgnKEIuYAXWtchzOBqlwjY9wBgQQRAFgAsMM5QBFvAL9+NP/y9+OU9pV2gl1ztMgH7HgAsiADIAoAFxhmKQAt48STd++riKNpXOyslV7tEwL4HAAsiALIAYIFxhiLQAj6Jov2/ml16vvj7KLqi9RqYq10iYN8DgAURAFkAsMA4QxFmAZ9F0f3skgwvZtEDJRWudomAfQ8AFkQAZAHAAuMMRZgFfBKfCzq7JtJc7drAXO0SAfseACyIAMgCgAXGGYogCzi9GlJWwOcznoijQ0JdwL4HAAsiALIAYIFxhiLIAk6rNytgvYsDc7VLBOx7ALAgAiALABYYZyhYwCzgTgl1AfseACyIAMgCgAXGGYogC3hxHO94lTXvmdpu0FztEgH7HgAsiADIAoAFxhmKIAs43fEqLeBVGXMnrA4JdQH7HgAsiADIAoAFxhmKMAv4fBbdfJ0U8JvbUbxDlgpfniptOAdhqQB4ALAgAiALABYYZyjCLODVS+AoOpjtXb+2+vOWlsiXp6e6FYywVAA8AFgQAZAFAAuMMxSBFvDyD7P8asBq/ZsUsGoFIywVAA8AFkQAZAHAAuMMRagFvHz79GDVvpdvPNOTSAtYsYIRlgqABwALIgCyAGCBcYYi2AIegbyA1SoYYakAeACwIAIgCwAWGGcoWMB6rAtYqYIRlgqABwALIgCyAGCBcYYi2AL+46cFnykdB1wuYJUKRlgqAB4ALIgAyAKABcYZikALeL0PVnJZYB2RagErVDDCUgHwAGBBBEAWACwwzlCEWcBnUTRBATuvYISlAuABwIIIgCwAWGCcoQiygBfH0d7jrwteKclsFrDjCkZYKgAeACyIAMgCgAXGGYogC/jiKD4XtDpNBey0ghGWCoAHAAsiALIAYIFxhiLQAtZ617lCcwE7rGCEpQLgAcCCCIAsAFhgnKEIsoAXx5MWsLMKRlgqAB4ALIgAyAKABcYZiiALeDmf8C1ohxWMsFQAPABYEAGQBQALjDMUYRbwxZHaJZBKdBWwkwpGWCoAHgAsiADIAoAFxhmKMAt4+eYo2r+b894oJ+JQqGCEpQLgAcCCCIAsAFhgnKEItICfTHMcsOMKRlgqAB4ALIgAyAKABcYZijALeD7RiTgcVzDCUgHwAGBBBEAWACwwzlAEWcDJiTiU3ncuISngQRWMsFQAPABYEAGQBQALjDMUQRbwOPtgCQt4QAUjLBUADwAWRABkAcAC4wxFoAU88XHAjioYYakAeACwIAIgCwAWGGcogizgyU/E4aiCEZYKgAcACyIAsgBggXGGIsgCXs6jWyOo9CngnSoYYakAeACwIAIgCwAWGGcowizgxfHefX2VfgW8QwUjLBUADwAWRABkAcAC4wxFkAW8eHQnivauT30ijsEVjLBUADwAWBABkAUAC4wzFEEW8MWRN8cBD6pghKUC4AHAggiALABYYJyhYAH7VcC9KhhhqQB4ALAgAiALABYYZyiCLOCR2K2Ae1QwwlIB8ABgQQRAFgAsMM5QsID12LWAxRWMsFQAPABYEAGQBQALjDMULGA9di9gYQUjLBUADwAWRABkAcAC4wwFC1iPIQUs6mCEpQLgAcCCCIAsAFhgnKFgAesxsIC3VzDCUgHwAGBBBEAWACwwzlCwgPUYXMDbOhhhqQB4ALAgAiALABYYZyhYwHq4KODOCkZYKgAeACyIAMgCgAXGGQoWsB5uCrijghGWCoAHAAsiALIAYIFxhoIFrIerAm6tYISlAuABwIIIgCwAWGCcoWAB6+GugFsqGGGpAHgAsCACIAsAFhhnKFjAergs4MYKRlgqAB4ALIgAyAKABcYZisAKePHo7ib+XA2pZwUjLBUADwAWRABkAcAC4wxFYAVcuwyDjxdj6FHBCEsFwAOABREAWQCwwDhDwQK2VMC1CkZYKgAeACyIAMgCgAXGGYrACnhUNAq40sEISwXAA4AFEQBZALDAOEPBAtZDqYDXFYywVAA8AFgQAZAFAAuMMxQsYD3UCjjvYISlAuABwIIIgCwAWGCcoQi3gBdfZ7z8T4Y+A65UMMJSAfAAYEEEQBYALDDOUARawG8eGt0Jq1LBCEsFwAOABREAWQCwwDhDEWYBV3eGvmK0gE+//FLngZfgxBJs374FEQBZALDAOEMRZgHPo2jv+p1Z/F+0d19LZYQC3n7N4IFwYgm2b9+CCIAsAFhgnKEIsoAXx9GV1/H/H8RdfEXpRFjjFPCpbgdzYgm2b9+CCIAsAFhgnKEIsoAvjvY+Wsbde2v1/5O4hlUYq4A1K5gTS7B9+xZEAGQBwALjDEWgBZzsd3UWXS3+r8F4BaxXwZxYgu3btyACIAsAFhhnKMIu4Pjd54sjrfegxyxgrQ7mxBJs374FEQBZALDAOEMRZAEvjpO3oM9ncQ9nbVzix998cHj4q9+Xb/r3/3p4+LPqTdsZuYBVKpgTS7B93y04CjRAFgAsMM5QBFnAy5Pk09/0o+C0hkv88OFhzF/82/qm3yW3HP7sn3upjF7ACh3MiSXYvucWXAUaIAsAFoKPMxZhFvD5LLrxLN4N+lZcxrW3oL84/MXvl3/++PAXf8pv+e7wZ3+7jG8qz7DtTFHAriuYE0uwfc8tuAo0QBYALAQfZyzCLOBV68bnvzqLor1ZlLwaXvP9B8lU+uHD4uXBjx8f/t0yuSn9U8o0Bey2gjmxBNv324KzQANkAcBC6HEGI9ACXv4hfuN5cZKcCKv6Avibw7/M/vyb7JYfPsxeKHxR3CRiqgJ22cGcWILt+23BWaABsgBgIfQ4gxFqAS+X/3PVu4sXBwf3avtAf5G9Kvgum1uVb1kpYGcVzIkl2L7fFpwFGiALABZCjzMY4RZwCz9+nL1T9/0H68/MUkpv4uV828WXE9P54AgO7gI9tRNC+k5s24RZwCc3nrV9q2NefbP5EqIzSVMXMCs4EDqz3ivQUzshpDPNcARZwBdH7aefLM2r2i6i31k4DMn1O9F8z06wfa8tuAs0QBYALAQeZzQCLeD2SwC3vmD47oOf9doH2o8CHtrBnFiC7XttwV2gAbIAYCHwOKMRZAFnZ8JqpG1efdP39a83BTyogjmxBNv32oK7QANkAcBC4HFGI8gCjq9B2PohcPNOo7/r37/+FPDp7h3MiSXYvt8WnAUaIAsAFkKPMxhhFvDbT6Lo8vW7Ge9VDkTKD5f8pnSIxo9fHP6810mwEnwq4F0rmBNLsH2/LTgLNEAWACyEHmcwgizgi6OoTPUD4c0TByUn8/tTfSPb8auAd6tgTizB9v224CzQAFkAsBB6nMFgAdcL+MePD39eO3XuNzv1r3cFfLpDB3NiCbbvtwVngQbIAoCF0OMMRpAF3M2fSxeP+f6D1cuG7GoyMRsHAnfhYQH3rmBOLMH2PbfgKtAAWcNjktMAACAASURBVACwEHycsWABb/Ln36wm06+SlwjJvPruEKiAT/t1MCeWYPu+W3AUaIAsAFhgnKFgAevhawH3qWBOLMH27VsQAZAFAAuMMxRhFvDbr8u8UlLxt4DlFcyJJdi+fQsiALIAYIFxhiLIAu7cCcsdPhfwqbCDObEE27dvQQRAFgAsMM5QsICDLeBTSQdzYgm2b9+CCIAsAFhgnKEIsoAXf/w04x9vR3v/9Nnr7XfZBf8L+HRrB3NiCbZv34IIgCwAWGCcoQiygMucz64o9a+NAj7t7mBOLMH27VsQAZAFAAuMMxTBF/ByHt1S2rKVAj7t6GBOLMH27VsQAZAFAAuMMxQsYL2XwIYK+LStgzmxBNu3b0EEQBYALDDOULCAuy4OPAxbBXza2MGcWILt27cgAiALABYYZyhYwOczFvCaugdOLMH27VsQAZAFAAuMMxTBF/DiJOJb0BUqHjixBNu3b0EEQBYALDDOUARZwItH+aWA796ZRdwJa4O1B04swfbtWxABkAUAC4wzFEEWcPVEHMEfhtRI5oETS7B9+xZEAGQBwALjDEXwBXz5nlb/2i7g07SDObEE27dvQQRAFgAsMM5QBFnAI2G9gFdwYkm2b9+CCIAsAFhgnKFgAesBUMCnX/a6evAOACx3AAsiANoLwALjDEWQBbx49N76fefzOz/hXtDtEvH/dJ6eFIDlDmBBBEB7AVhgnKEIsoAr597giTg6JbI/dZ6hJcRyB7AgAqC9ACwwzlCwgHkijk6J9V91niSA5Q5gQQRAewFYYJyhCK2Aa5cCVj0OCayAT1U6GGC5A1gQAdBeABYYZyhCK+Dl2WYBP1CSwivgU/cdDLDcASyIAGgvAAuMMxTBFfDiX+LTX+1dL86F9f7nWlKQBXzquIMBljuABREA7QVggXGGIrgCjtHb76oCagHHOHuSAJY7gAURAO0FYIFxhiLIAq4chqQHcgHHOHmSAJY7gAURAO0FYIFxhiLIAh4J9AKOGfwkASx3AAsiANoLwALjDEVgBVy6DtIarZfDIRRwzKAnCWC5A1gQAdBeABYYZygCK+Cmo5AiHgfcISH8uZ2fJIDlDmBBBEB7AVhgnKFgAbOAOyV6/OxOTxLAcgewIAKgvQAsMM5QBFbAoxJaAZ/u0sEAyx3AggiA9gKwwDhDwQLWI8ACPu3dwQDLHcCCCID2ArDAOEPBAtYjzAI+7dfBAMsdwIIIgPYCsMA4QxFmAb/9uswrJZVgC/i0RwcDLHcACyIA2gvAAuMMRZAFXNsVizthdUgMuK/oSQJY7gAWRAC0F4AFxhkKFjALuFNi4P23PkkAyx3AggiA9gKwwDhDEWQBL/74acY/3o72/ukznoijXcLBNjqfJIDlDmBBBEB7AVhgnKEIsoDLnM+0LgfMAq7Q8iQBLHcACyIA2gvAAuMMRfAFvJxHt5S2zALeZONJAljuABZEALQXgAXGGQoWsN5L4PjzZcf9VcVgAcdUniSA5Q5gQQRAewFYYJyhYAHrXRw438lLocNSjBZwTPEkASx3AAsiANoLwALjDAUL+HymuRe0agkbLuCY5EkCWO4AFkQAtBeABcYZiuALeHESab0FnZeXWgkbL+AYhOUOYEEEQHsBWACI86kXcfaDIAu4dFXgO7NolJ2wVEoYoIBjD0pPf4H9icUCdqWgLmA/C+oWTlnAa4Is4OqJOEY7DMl5CYMUcIzSryDB/MRiATtTUBewnwVlC/Fq9yLOfhB8AV++p9W/jYchOS1hoAJO0fk9GJ9YiYS6ggCA9gKwYDzOyTr3Is5+EGQBj0RbeTkrYbgCTnH9e7A9sVIJdQUBAO0FYMF2nNMV7kWc/YAFrEdXeTkpYdACTnH3ezA9sTIJdQUBAO0FYMFynPO17UWc/YAFrMe28hpcwtAFnOLi92B4YhUS6goCANoLwILhOBer2os4+0HgBfy15sYl5TWohAMo4JRhvwe7E2stoa4gAKC9ACzYjfN6PXsRZz8ItYAXT995nuyNdeOZmoa0vHYu4WAKOGXX34PZiVWSUFcQANBeABasxrm8kr2Isx8EWsBns+QqwPHu0HsPtET6lNdOJRxYAaf0/z0YnVgVCXUFAQDtBWDBaJwra9iLOPtBmAV8PssO//3jw1m095GSSt9q6V3CQRZwSp/fg82JVZVQVxAA0F4AFkzGubZ6vYizH4RZwCfRfv7O8+I4uqqksku19CrhgAs4RfZ7sDix6hLqCgIA2gvAgsU419etF3H2gyAL+OKo9KpX92IMuyAu4eALOGXb78HgxNqQUFcQANBeABbsxXlzxXoRZz8ItIBLnat3OcIh1SIqYRbwBk2/B3MTq0FCXUEAQHsBWDAX54ZV6kWc/SDQAvb6FXDO1hJmAbdQ/T1Ym1hNEuoKAgDaC8CCsTg3rk8v4uwHQRbw8qT0ue+JV58B1+ksYRZwJ/nvwdbEapZQVxAA0F4AFmzFuXllehFnPwizgM+i6Mar5G9vn0SR1nFIrqqltYRZwDKJU+WrLdkfuiIA2gvAgqUCbluRXsTZD8Is4NXL3ijaOzg4mK3+1HoB7KyAYxpLmAUsk6h97fr3DDB0RQC0F4AFQwXcuiK9iLMfBFrAi0/yRtv7azUR19WyUcIsYJlE463ufs8AQ1cEQHsBWDBTwB0r0os4+0GgBbyq4Jd3Vq+Ar/9a7WrA7gs4plLCLGCZRMf3XPyeAYauCID2ArBgpYC7VqQXcfaDYAt4BLSqpShhFrBMQvhzu/6eAYauCID2ArBgo4C7V5oXcfYDFrAemtXi5HrCAkIq4DJ9fs8AQ1cEQHsBWDBRwFtWlxdx9oNgC/jt04N4P6x7r/QktKtljBIOtYDbaPo9AwxdEQDtBWDBQAFvXUVexNkPQi3ged5e0S01jTHKS7uEWcDt5L9ngKErAqC9ACz4X8DbV44XcfaDQAs47t/L1+/euabZwGOVl2YJs4AF2/9S9TDjGC8mFkB7AVjwvYAlK8aLOPtBmAV8PouupJdDenPsz+UId5n8OVolzAIWbL9dwFWUvJhYAO0FYMHzAhatGC/i7AdhFvBJejXgGM8uR9iL2uTXKGEWsGD7/QX6RsmLiQXQXgAWvC5gYfy9iLMfBFnAi+PKxRiuKB0LPEV5uS5hFrBg++4E2qLkxcQCaC8ACz4XsDTmXsTZD4IsYAOXIxTRMvldljALWLB9fQteTCyA9gKw4G8BG4uzH7CA8Qo4xlUJs4AF22cBOxJgAUsktLe/o4C1OPtBkAW8OC5dAeksQnoLukxxpNWAGp7agxMJ7e2zgB0JsIAlEtrb30nAXpz9IMgCBt0Jq4koGlbDPngYLKG9fRawIwEWsERCe/u7CBiMsx+EWcDns2j/8+RvX93GOAypm91L2B8PAyS0t88CdiTAApZIaG+/v4DJOPtBmAWcngjr4OBA9VRYvpXXLiXsm4edJLS3zwJ2JMAClkhob7+3gM04+0GgBbx8McuvB3xfTcPH8upbwj566C2hvX0WsCMBFrBEQnv7PQWsxtkPQi3g/HrAj41dD7jCjpO/Twn76qGXhPb2WcCOBFjAEgnt7fcSsBtnPwi2gEfA5/KSlrDPHsQS2ttnATsSYAFLJLS330fAcJz9gAWsh+/lJSlh3z2IJLS3zwJ2JMAClkhob18uYDrOfhBYAS8e3X3vdfz/Mu+BHgcsYVsJW/CwVUJ7+yxgRwIsYImE9valAsbj7AeBFfDFUXTpefz/MohnwupDVwlb8dApob19FrAjARawREJ7+zIB83H2AxYwCzimrYQteWiV0N4+C9iRAAtYIqG9fZGA/Tj7QWAFPCrWyqvpdB3WPDRKaG+fBexIgAUskdDevkAAIc5+wALWw2J51U9dadHDhoT29lnAjgRYwBIJ7e1vF4CIsx+wgPWwW17rErbroSShvX0WsCMBFrBEQnv72wRA4uwHgRfw15obt15eQy7iIIYFLMGLiQXQXgAWJi9glDj7QagFvHj6zvNkn6wbz9Q0rBdwjHoJs4AleDGxANoLwMLEBYwTZz8ItIDPZsm+z/Hu0HsPtv/4biAUcKygWsIsYAleTCyA9gKwMG0BA8XZD8Is4PNZlF4R+I8PZyFcjnCARPanWgmzgCV4MbEA2gvAwpQFDBVnPwizgE+i/fyd58VxdFVJBamAY1RKmAUswYuJBdBeABYmLGCsOPtBkAV8cVR61Xs+44k4OiRqXzsvYRawBC8mFkB7AViYrIDR4uwHgRZwqXMrXzgFsYBjnJYwC1iCFxMLoL0ALExUwHhx9oNAC5ivgKUSLbc7K2EWsAQvJhZAewFYmKaAAePsB0EW8PKk9LnvCT8D7pLo+F7TqSv7C7CABXgxsQDaC8DCFAUMGWc/CLOAz6Loxqvkb2+fRJHWcUjoBRxTP3Vlf4HpPQzePgvYkQALWCKhvf26AGic/SDMAl697I2ivYODg9nqT60XwEEUcMqAEvbGw4Dts4AdCbCAJRLa268KwMbZDwIt4MUneWPs/bWaSDgFnLJTCXvmYafts4AdCbCAJRLa268I4MbZDwIt4FUFv7yzegV8/dev9SRCK+CY3iXsoYfe22cBOxJgAUsktLdfEkCOsx8EW8AjEGIBx/QqYU899No+C9iRAAtYIqG9/UIAO85+wALWI9QCjhGXsMcexNtnATsSYAFLJLS3nwuAx9kPgi3gt08P4v2w7r3Skwi5gGNEJey5B9H2WcCOBFjAEgnt7acC8HH2g1ALeF7st3tLTSP0Ao7Zun+0AQ9bt88CdiTAApZIaG8/Fgggzn4QaAHH/Xv5+t071zQbmAWc0nmssBEPndtnATsSYAFLJLS3/61y/XoSZz8Is4DPZ9GV9HJIb455OcJOCWdbailhUx5ats8CdiTAApZIaG//2zDi7AdhFvBJejXgGF6OsFvC8fY2Stigh43ts4AdCbCAJRLK2w8lzn4QZAEvjisXY7iidCwwC7iZSgkb9VDZfiATC6C9ACwoewgnzn4QZAHzcoRyCaXtFiVs2EOx/UAmFkB7AVjQ9RBQnP2ABcwC7pRQ3LazSxpugQXsBoD2ArCg6SGoOPtBkAW8OC5dAeks4lvQHRLK2x+jhFnAbgBoLwALeh4Ci7MfBFnA3AlLLqEu8KWb6wp3Sehsdr39QCYWQHsBWNDyEFyc/SDMAj6fRfufJ3/76jYPQ+qUUBdIFTqPFR4q4XqD9e0HMrEA2gvAgpKH8OLsB2EWcHoirIODA9VTYbGAJQJlBZ0SZgG7AaC9ACyoeAgxzn4QaAEvX8yySb93X02DBSwR2FRwXcIsYDcAtBeABQUPYcbZD0It4Px6wI95PeBuCXWBZgWXJcwCdgNAewFYcO8h0Dj7QbAF7IZvu/iSDGL9hvTUj2RyOnPmcJx16xBAvIuzuzRbgAWsB18BSwS6FVzsnMVXwG4AePkIYMGth4Dj7AdhF/Dij59+prf1qcvLiYS6gERhWAmzgN0A0F4AFlx6CDrOfhBqAb/5f18vlxe3VyN9X+soJBawSECusGsJs4DdANBeABbceQg8zn4QaAHPo/j8kyfJPNc6EyULWCTQT2GXEmYBuwGgvQAsuPIQfJz9IMwCPktqN74q8Os3R2oHAvtWXjtJqAv0V+hbwixgNwC0F4AFNx4YZ08Is4DTU1GeJSfB4rmgOyXUBXZT6FPCLGA3ALQXgAUXHhhnbwiygLPrAac1zKshdUqoC+yuIC1hFrAbANoLwMJwD4yzRwRZwGnnXhwll2FgAXdKqAsMU5DsH80CdgNAewFYGOqBcfaKgAv4fJZclJAF3CmhLjBcYduxwixgNwC0F4CFYR4YZ88IsoDTt6Dn6XWQ+Blwp4S6gCuF9hJmAbsBoL0ALAzxIMpaKHH2gyALeHkSXY13f46bl3tBd0uoC7hVaCphFrAbANoLwMLuHoRZCyXOfhBmAZ+lU/rBcvEw4vWAOyXUBdwr1EuYBewGgPYCsLCrB3HWQomzH4RZwOn1gK8mO2LtPdASsVheGxLqAjoK5TekWcBuAGgvAAu7eeiRtVDi7AeBFvDyzaP3Hq/+uPjljWdqGlbLqyKhLqCn4OJCDhJCmVgA7QVgYRcPvbIWSpz9INQCHgPL5VVIqAtoK+iXcCgTC6C9ACzs4KFf1kKJsx+wgPVoi5mzHLOAZRKnu1/IQbT9QCYWQHsBWOjtoW/WQomzH7CA9ZDEbFCOWcAyiexPrRIOZWIBtBeAhZ4e+mctlDj7AQtYjyExE+WYBSyTKP1d4w3pUCYWQHsBWOjnYYeshRJnP2AB6+EqZlMuFbQCjnG9c1YoEwugvQAs9PGwU9ZCibMfsID10IjZ2EsFsYBT3JVwKBMLoL0ALMg97Ji1UOLsByxgPbRjxgIWSnR8z0UJhzKxANoLwILUw85ZCyXOfsAC1mPM1a61VNALOGZoCYcysQDaC8CCzMOArIUSZz9gAesx1Wp3uVRCKOCYIW9IhzKxANoLwILEw6CshRJnP2AB6zHtanezVEIp4Jhdd84KZWIBtBeAhe0eBmYtlDj7QZAFfPHLm69GkPFhtQ9dKiEVcEr/Gg5lYvmQ56EK6gKTF/DgrIUSZz8Is4CPoujyPaWrAK/xbbXvslTCK+CUPjUcysTyLc+7KKgLTFzADrIWSpz9IMgCXjxJBuv+Y90O9nm1S5dKqAWcIivhUCaWz3mWKqgLTFrATrIWSpz9IMgCXvHVu8lcfeexoob/q12wGrXXotcFnLKthEOZWP7nebuCusCEBewoa6HE2Q9CLeAVL9MO1rseoZXV3rkatdeigQKO6SrhUCaWlTx3KagLTFbAzrIWSpz9IOACXi4XL2/HQ3Xvns4uWRZX+8Zq1F6LRgo4pu0N6VAmlsU81xXUBaYpYJdZCyXOfhB0Aa94+3CWvBWt8TLY7movrUbttWiogGOads4KZWLZzfNaQV1gigJ2m7VQ4uwHQRfw4um1YpredL9566s9WY3aa9FYAadUSziUiWU9z0sIC3UPzrMWSpz9INwCzt5/TvaFfvskim45V8BY7cpr0WQBp+QlHMrEAsgzgIWaB/dZCyXOfhBoAeftWxwNfBZdcX5MEtJqV1uLhgs4xsG1HCR4MbEA8gxgoeJBI2ss4DEJsoAXfx/Vd766OLr03LUM2mpXWe/GCzhmwGmkpXgxsQDyDGCh5EEnayzgMQmygOMzYe1VDz+6OOIr4EaJhtucrkWAAl5Z2PU00lK8mFgAeQawoP6mFAt4TAIt4Buf125afOb+UCSk1b6Jo7WIUcApejXsxcQCyDOAhcyD23yVYQGPSZAFPBI4q72dwWsRqYBTNGrYi4kFkGcAC4kHV7lqggU8JkEW8OLRe+v3m8/v/ETplNAoq307A9YiXgGnuC1hLyYWQJ4BLKgfmMACHpMgC7iyx5XG7lcpGKtdzk5rEbWAU1yVsBcTCyDPABaWE8bZFV7E2Q9YwOczFnCHRO979FyL2AUc46KEvZhYAHk2b+F08ji7wIs4+0FoBRzvAF3H/f7PKeZX+3J3D+K1iF/AMUPfkPZiYgHk2biFNG67RUgMC3hMQivg5dlmAT9QkjK+2lOJAfcVrcUwCjhmyM5ZXkwsgDxbtlDErW94esICHpPgCnjxL3fv3pntXb+b8379iCRnWF7thcTA+/uw3L2aWLvVsBcTCyDPdi2U4tYjbbvAAh6T4Ao4Rm+/qwp2V3tJwsE2pl7uHk6svjXsxcQCyLNVC5W49U5bP7RX5CryXsTZD4Is4MphSHpYXe0VCWdbmmi5n3o8seQl7MXEAsizTQu1uO2YNimqKzLLu8KTZBQ+FXrYXO01CcfbG3e5ZxLa2x8mIClhFrAjBXUB9wobcRuUtu3orcgi6F7E2Q8CK+DFo7urF7+r/5fRejlscbVvSChsc6TlvpbQ3v5wgW0l7MXEAsizPQsNcRuctm6UVmQ54V7E2Q8CK+CLo+jS8/qxSDwOuENCabvay72MkYnV9Ya0FxMLIM/WLDTGzUna2tFYkbVgexFnP2ABs4A7JRS3rbbcaxiaWG07Z3kxsQDybMtCS9ycpa1l+64FNvPsRZz9ILACHhVbq71FQl2ABbzBZg17MbEA8mzJQmvcHKdtY/tOBRrf1PEizn7AAtbD0mpvlVAXSBVcrvk6tiZWQbmGvZhYAHk2Y6ErbippK23f+Rs6G7d7EWc/YAHrYWa1d0moC6wVnK37GoYm1iYsYKcK6gJOFLrjppi2ZPuOBDr2K/Qizn4QWAHX9n/mXtBbJdQFqgpu1n4VKxOrAy8mFkCeLVjYGjflrDmJc/de/V7E2Q8CK+CmazFwJ6wuCXWBTQUH67+CiYnVjRcTCyDP3luQxE05a8PjvOWYOk/i7AcsYBZwp4S6QLPC0BlQxv+JtRUvJhZAnv22IMuC53He2r6nnsTZDwIr4FHxe7ULJdQF2hUGzYESnk8sCV5MLIA8e2xBnAWf4yxp31NP4uwHLGA9PF7tcgl1gW6F3WfBGp8nlhAvJtbUWXChoC6wm0KfLHgbZ2H7nnoSZz9gAevh62rvJaEusF1hx3lQ4O3EkuPFxPIhC0MV1AV2UeiXBT/jLG/fU0/i7AeBFTDPBd1XQl1AprDLTMjxc2L1wouJ5UsWhiioC/RX6JsFD+Pcq31PPYmzHwRWwDwVZV8JdQG5Qu+5kOHhxOqLFxPLpyzsqqAu0FNhhyz4Fue+7XvqSZz9gAXMAu6UUBfop9BvNqT4NrF2wIuJ5VsWdlFQF+ilsFMWvIrzDu176kmc/SCwAh4Vz1b7bhLqAkG+Z9cXLyaWj1noq6AuENIbOru176kncfYDFrAePq32nSXUBQLebVSOFxPL1yz0UVAXkCrsngVP4rxz+556Emc/CLmAF1/rbt+f1T5AQl1gdwXxRNlpSMhhAbsSCKaAh2TBhzgPad9TT+LsB8EW8Mt3kwjdeKYn4ctqHyShLjBMQTRRdp0TQljArgTCKOCBWZg8zgPb99STOPtBoAW8OC52wbqpdBCSJ6t9qIS6AM9eL8CLiWUhC9sU1AW2KgzOwrRxHt6+p57E2Q/CLOC4f/eu//rT395ZZemqlooHq324hLqAG4XOiTJsWmyFBexKAL6AXWRhwjg7ad9TT+LsB2EW8FkU3Ur/tvgkih4oqUy92p1IqAu4U2idKIMHRjcsYFcC2AXsKAtTxdlV+556Emc/CLKAVy+AbxVfnKi9BAYYWOY8NE4UFzOjAxawKwHgAnaXhUni7LB9Tz2Jsx8EWcAXR3sfFV+cz3gijg4JdQH3ChsTxdHYaIMF7EoAtYCdZmH8OLtt31NP4uwHgRZwqXMrXzgFYGCZ9VCZKO4mRyMsYFcCkAXsOgsjx9l5+556Emc/CLKAF8eVV8BXeDGGdgl1AT2FYqI4HR6bsIBdCeAVsEIWxoyzRvueehJnPwiygJfz0ue+89LnwW4BGFjWPaQTxfX8qMECdiUAVsA6WRgtzkrte+pJnP0gzAK+OIp+mr3snatdiwFhYAF4YAG7wn4WxrSgloVx4qzXvqeexNkPAivg/ErAd9LjgP/17iyKrvN6wB0S6gIjeNCZIgUsYFcCMAWsmYURCli1fU89ibMfBFbAtQsR8nKEWyXUBcbxoDZLTlnA7gQwClg5C8ppi7Tb99STOPsBC5gF3CmhLjCeB61xwgJ2JQBQwPpZUFQYo3xjvIizHwRWwKMCMLDgPGiMExawKwHrBTxKFpQUivINJc5+wALWA2BgQXpwPU5CmVgAWdAUGCsLGgrlV76hxNkPWMB6AAwsWA8ux0koEwsgC2oCI2bBuULtfedQ4uwH4Rbw4uuMl/+JnwG3S6gLTOfB1TgJZWIBZEH9tGvmCnjzU99Q4uwHgRbwm4fcCUsmoS4wrQcX4ySUiQWQBQWB0bPgUKFxn6tQ4uwHYRZwdWfoKyzgdgl1gck9DB4noUwsgCy4FpgiC64U2vZ4DiXOfhBmAc/j83DcmcX/RXv3tVQABlYgHoaNk1AmFkAWXApMlQUnCh3HG4USZz8IsoAXx9GV1/H/H8RdrHUtBoSBFY6HAeMklIkFkAVXAlNmYbhC99G+ocTZD4Is4Ox6wOllGE7iGlYBYGAF5WHXcRLKxALIgguBqbMwUGHruTZCibMfBFrAyX5XZ8k1kc5KV0ZyC8DACs7DLuMklIkFkIWhAj5kYYiC5ExXocTZD8Iu4Pjd54sjXg+4Q0JdwD8PfcdJKBMLIAtDBES/KJ8LWHieyVDi7AdBFvDiOHkL+nwW93DWxgoADKxgPfQZJ6FMLIAs7Cog/kV5W8DyszyHEmc/CLKAlyfJp7/pR8FpDWsAMLCC9iAdJ6FMLIAs7CTQ5xflZwH3usZCKHH2gzAL+HwW3XgW7wZ9Ky5jvgXdIaEu4LcHyTgJZWIBZKG/QM9flH8F3PsKR6HE2Q/CLOBV68bnvzqLor1ZlLwa1gBgYNHDcvsMDmViAWShp0D/X5RfBbzT5QVDibMfBFrAyz/EbzwvTpITYfE44A4JdQETHjixlhBZ0N4h3qMC3vnavqHE2Q9CLeDl8n+uenfx4uDgnlb/IgwseijgxALIgvYh4b4U8K7lm2w/kDj7QbgFrA/AwKKHMoFPLIAsSAQG/aJ8KOAh7XsaTpz9IOQCXnytu32AgUUPNUKeWABZ2CYw+Bc1eQEPbN/TcOLsB8EW8Mt3k6DeeKYnATCw6KGBUCcWQBa6BJz8oqYt4OHtexpOnP0g0AJeHBcXI7zJz4C7JNQFbHoIcWIBZKFNwNkvaroC3nmnq43tBxJnPwizgOP+3bv+609/e2eVWK1TQSMMLHroILSJBZCFJgGnv6hpCthZ+SbbDyTOfhBmAZ8VB/8uPol4NaQuCXUB0x6CmlgAWagKKPyixi9gp+WbbD+QOPtBkAWcngIr44RXQ+qSUBew7iGciQWQhVxA7Rc1cgG7Lt9k+4HE2Q+CLODsesApPBd0p4S6gH0PcdpmxAAAIABJREFU3wYysQCykAho/qLGLGCN9j1lAY9LoAVc6lxeDalTQl3AvoeVhSAmFkAWvtX+RY1XwDrte8oCHpcgCzi7HGHK+YwXY+iQUBew7yGzAD+xrGfhdNjF7EWMVMBq7XvKAh6XIAt4OS997jvnxRi6JNQF7HsoWYCeWJazkD2PCAXsfK+rGizgMQmzgC+Oop9mL3vnkdY70KYHViGhLmDfQ80C7MSymoXS82i9gLXLN4YFPCaBFfDi0d2EO+lxwP96dxZF19/jW9DtEuoC9j1sWsCcWBazUHseLRdwXr7aHljAYxJYAa9e+m7CnbA6JNQF7HtotAA4sWxlofF5tFrA5Ve+LGAkWMAs4E4JdQH7HtosoE0sK1noeB4tFnD9bWcWMBKBFfCoWBlYnRLqAvY9dFiAmlj+Z2Hr82itgJs+82UBI8EC1sP/gSWQUBew72GLBZiJ5XMWhM+jqQJu2eGKBYwEC1gPnweWWEJdwL4HgQWIieVrFno8j3YKuH13ZxYwEsEW8NunB1G0d3DvlZ6ErwOrl4S6gH0PQgvmJ5aPWej5PBop4M6DjVjASIRawPNiFyyt03D4ObB6S6gL2PfQw4LpieVbFnZ4Hi0U8LZDfVnASARawHH/Xr5+9841zQb2bWDtJKEuYN+Dfm/4MbH8ycLOk9/3ApacaIMFjESYBXw+i648S/725jgqnRc65cfffHB4+Kvf12794cO/7Kniz8AaIKEuYN+D/junAyeWo0D7kIWBk9/nApae5YoFjESYBXwSFRdgWBzXrwf8w4eHMX/xb9WbvzhkAWsI2Pewm4XxJparQE+bBSeT39cC7nOKSRYwEkEWcPfVkL44/MXvl3/++PAXfyrd+OMXhyxgFQH7Hna2MNLEchXoqbLgcPL7WMB9z+/MAkYiyALuvB7w9x8kLxV++PBn/7y+8d//70MWsI6AfQ9DLIwwsZwFevwsOJ/8vhXwLhdXYAEjwQKuF/A32Vz65vBvSrcd/pf/xQJWEbDvYaAF7YnlLNBjZkFp8vtUwLte2YgFjESQBbw4jh4UX5xF1begvzj8u+TP70rz6Zuf/7fK1zJYXhIB+x4cWNCcWM4Crf08jjD6vSngAZcVZAEjEWQBd+2E9ePH2Tt1339Q+cyseV59S4gjvuyg+56dWe8V6HGcbtBlHZD8uoLB0hmGLbMbjDAL+HwW7X+e/O2r27XDkFjAZDp2mliWC3jMse8HwbfvlyzgEmEWcHoirIODg81TYZXmVfW4Db4FrSNg34NbC67fs3MXaLfPY4NL/LegB7zzvJYYdG/B9vkW9IgEWsDLF7NsLezdr36j3yvgTlheEgH7HpxbcDqx3AXalc3WuYxdwLvudLUhMXQD27bPAh6RUAt4uXh5Z/UK+Prj17XbWcBVCXUB+x40LLibWH4UsGgu4xawq/JNJFxspGv7LOARCbaAW2naabTp6+2wvCQC9j0oWXA1sZwFercH0WMuYxawy/JNJFxtqG37LOARCbOAT248a/1efrhk+bDJGBawjoB9D4oWXEwsZ4Hu+yB6z2W8AnZdvomEy401bZ8FPCJBFvDFUek44DqNJw5asoC1BOx7ULYwdGI5C7T8Qew4l7EKWKN8EwnXG6xvnwU8IoEWcPXkVxV+/Pjw55unzmUBawnY9+C5BWeBljyIQXMZp4C1yjeR0Nhoefss4BEJsoArF2PY4M+li8d8/0HxsoEFrCNg34PvFlwFuu1BOJvLGAWsWb6JhNaG8+2zgEckyAJezvPLATfy59+sxtWvkpcLLGB1AfsevLfgKNDlB6Eyl+0XsHb5xrCAkQizgN9+EkWXr9/NeK9+KJIjWF4SAfseACyISB+E4ly2XcBjlG8MCxiJIAv44igq0/GB8CBYXhIB+x4ALIiY+jRSLhSUtluU7wjtxQJGggXMAu6UUBew7wHAgggWcCOVV74sYAlexNkPgizgkWB5SQTsewCwIIIFvMHG284s4DbKSfIizn7AAtaD5SURsO8BwIIIFnCFxs98WcCbbCbJizj7QdAF/LXu5lleEgH7HgAsiGABF7TucMUCLtOWJC/i7AfBFvDLd5NrIXWck3IwLC+JgH0PABZEsIATOvd2ZgFLkuRFnP0g0AJeHBe7YN1UOgiJ5SUTsO8BwIIIFvD2Q43CLOC+SfIizn4QZgHH/bt3/Z8+/e2dWRRd1VJheUkE7HsAsCAi8AIWHecbWgHvliQv4uwHYRbwPIp+mr7wXXwSdVyYYRgsL4mAfQ8AFkQEXMDik2yEU8BDkuRFnP0gyAJevQC+VXxxovYSmOUlEbDvAcCCiEALuNcZrrAL2FWSvIizHwRZwBdHpYsxnM94Io4OCXUB+x4ALIgIsIB7n14StYDdJsmLOPtBoAVc6tzOaxMOguUlEbDvAcCCiMAKeKdzO+MVsEaSvIizHwRZwJXLEZ7PrvBiDO0S6gL2PQBYEBFQAe98YQWMAtZOkhdx9oMgC3g5L33uO+dnwF0S6gL2PQBYEBFIAQ+6qpH1Al6GE2c/CLOAF8fF4b9ztWsxsLxEAvY9AFgQEUABD76koN0CLn7NocTZD4Is4MWj+Pjf6/c+/df4z3e0rgrM8pII2PcAYEEEeAE7uZ6vrQJu/DWHEmc/CLKAa5cj1LooIctLImDfA4AFEcAF7KR8EwEjBdzxaw4lzn7AAmYBd0qoC9j3AGBBBGgBOyvfRMD7At76aw4lzn4QZAGPBMtLImDfA4AFEYAF7LR8EwGPC1j4aw4lzn7AAtaD5SURsO8BwIIIsAJ2Xr6JgJcF3OvXHEqc/YAFrAfLSyJg3wOABRFABaxSvomANwW88685lDj7AQtYD5aXRMC+BwALIlAKWKt8EwEPCnjgrzmUOPsBC1gPlpdEwL4HAAsiEApYs3wTgUkL2MmvOZQ4+wELWA+Wl0TAvgcACyKsF7Da+84lpipgh7/mUOLsByxgPVheEgH7HgAsiLBcwGOUb8z4Bez81xxKnP2ABawHy0siYN8DgAURVgu4VL5WLVQkir/p/JpDibMfsID1YHlJBOx7ALAgwmJ71V75WrSwIXGqVb0pocTZD1jAerC8JAL2PQBYEGGrvdbnuCsrOBRoRL2AGWcsWMB6sLwkAvY9AFgQYaW9yieYrSs4EehAsYDzXwPjjAQLWA+Wl0TAvgcACyIstFdr9WYKgwW2oFLA1V8D44xE8AW8+OOnnzm/EGEKy0siYN8DgAURvreXYE9n3y3Uafo1MM5IBF/AF0cK10FKYXlJBOx7ALAgwuf2Eh5m5LOFKu2/BsYZCRYwC7hTQl3AvgcACyJ8ba8ex/j6aqHMtl8D44xE8AXMt6C7JdQF7HsAsCDCx/bqeYINHy2skf0aGGckgi9gRVheEgH7HgAsiPCtvXY4u5VvFnL6/BoYZyRYwHqwvCQC9j0AWBDhT3tt2de5Q6HvQ+pL7wLu/2tgnJFgAevB8pII2PcAYEGED+3VcYyvSGGH+/QTkCvs+mtgnJEIrYDffr3JKyUtlpdEwL4HAAsipm6vQdWbKex8T6mATGHIr4FxRiKwAr44ijbhXtAdEuoC9j0AWBAxZXs5upqRDwU89NfAOCPBAmYBd0qoC9j3AGBBxFTt5ah8EwUH2+gW6FZw8WtgnJEIrIAXf/w04ZMo2nvvnz799B+vRXv3eBhSh4S6gH0PABZETNFeDss3UXC0nXaBNgV3vwbGGYnACjhj9UL4VvbXF2ovgFleIgH7HgAsiBi7vRyXb6LgcFvNApsKrn8NjDMSYRbwSdG/y+U8uqqkwvKSCNj3AGBBxJjtpVC+iYLj7W0KlBV0fg2MMxJBFvDieO+j4ovz2RW+Bd0uoS5g3wOABRHjtJeDfZ07FBS2WRVIFTR/DYwzEkEW8MVR6W3nyhdOYXlJBOx7ALAgQru9Bh7jK2GMAtb+NTDOSARawJVXwCzgDgl1AfseACyI0Gwv9epN0S5ghCwAWDBEkAW8PCl97nvCz4C7JNQF7HsAsCBCq72K6lW5mn0FTYHkOQLIAoAFQ4RZwGdRdDP94HfxJIoeKKmwvCQC9j0AWBCh0V6V171WC7j0HAFkAcCCIcIs4NXL3ih65+7du9dWf97UEmF5SQTsewCwIMJtezW86WyvgDeeI4AsAFgwRKAFHL/wzbi1/ad3hOUlEbDvAcCCCFft1bqzla0CbnyOALIAYMEQgRbwcvn2afzq9/I9rSsxLFleMgH7HgAsiHDRXp07W9kp4NbnCCALABYMEWwBjwDLSyJg3wOABRFD22vrfs42CrjzOQLIAoAFQ4RcwAvFV78xLC+JgH0PABZEDGkv0UFG/hfw1ucIIAsAFgwRbAG/fDe+DNLFL+8pnQZryfKSCdj3AGBBxG7t1eMIX78LWPQcAWQBwIIhAi3gdCesVQEfRfta12JgeYkE7HsAsCCib3v1PrOVvwUsfo4AsgBgwRCBFvBJFO3/1ezS88XfR5HWqaBZXiIB+x4ALIjo0147ndnKxwLu+RwBZAHAgiHCLOCzKLqfnQT6xYwn4uiSUBew7wHAgghpe+18Ukm/Cnin5wggCwAWDBFmASeXI8yuwsDLEXZKqAvY9wBgQYSkvQad0dmfAt75OQLIAoAFQwRZwOnlCLMC5sUYOiXUBex7ALAgoru9HFxOwY8CHvQcAWQBwIIhgizgtHqzAublCDsl1AXsewCwIKKtvZxdRnD6Ah78HAFkAcCCIVjALOBOCXUB+x4ALIjYbC9n1ZvV46QF7OQ5AsgCgAVDBFnAi+N4x6usec/UdoNmeUkE7HsAsCCi3F6Oqzerx8kK2NlzBJAFAAuGCLKA0x2v0gJelTF3wuqQUBew7wHAgoi8vRSqN6vHSQrY6XMEkAUAC4YIs4DPZ9HN10kBv7kdxTtkqcDykgjY9wBgQUTcXkrVm9Xj6AXs/DkCyAKABUOEWcCrl8BRdDDbu35N83qELC+JgH0PABZEqJZvUo+jFrDKcwSQBQALhgi0gJd/mOXjhNcD7pRQF7DvAcCCCNXyTepxtAJWe44AsgBgwRChFvDy7dOD1Ty5fOOZngTLSyJg3wOABRH69ThGAes+RwBZALBgiGALeARYXhIB+x4ALIiwXsBLxlm0ffsWDBFmAZ9ovvAt4GqXCNj3AGBBhOUCziwwzoLt27dgiCAL+OJI7QIMZbjaJQL2PQBYEGG1gEsWGGfB9u1bMESgBax18qsKXO0SAfseACyIsFjANQuMs2D79i0YIsgCTi/GoA5Xu0TAvgcACyKsFXCDBcZZsH37FgwRZAEv59GVET4E5mqXCNj3AGBBhKUCbrHAOAu2b9+CIcIs4LefRNHl63cz3uO5oNsl1AXsewCwIMJKAXdYYJwF27dvwRBBFvDFUflc8hGvhtQhoS5g3wOABREWCniLBcZZsH37FgzBAmYBd0qoC9j3AGBBhO8FLLDAOAu2b9+CIYIs4JHgapcI2PcAYEGEzwUstMA4C7Zv34IhWMB6cLVLBOx7ALAgwtcC7mGBcRZs374FQ4RZwK9GUeFqlwjY9wBgQYSPBdzTAuMs2L59C4YIr4AXT+ILIV2+r7TrcwmudomAfQ8AFkT4VsA7WGCcBdu3b8EQwRXweX4dwn31k2FxtUsE7HsAsCDCpwLe0QLjLNi+fQuGCK2Akx2gL//v11b/v6L9GpirXSJg3wOABRG+FPAAC4yzYPv2LRgitAKeR1FyGsqXqxfC2hdk4GqXCNj3AGBBhA8FPNAC4yzYvn0LhgisgBfHee+umviqshhXu0TAvgcACyKmLmAHFhhnwfbtWzBEeAWcXYfhfKb+HjRXu0TAvgcACyKmLGBHFhhnwfbtWzBEYAV8cZSf92r9NzW42iUC9j0AWBAxVQE7tMA4C7Zv34IhWMB6cLVLBOx7ALAgYooCdmyBcRZs374FQ7CA9eBqlwjY9wBgQcTYBaxggXEWbN++BUOwgPXgapcI2PcAYEHEmAWsZIFxFmzfvgVDsID14GqXCNj3AGBBxFgFrGiBcRZs374FQ7CA9eBqlwjY9wBgQcQYBaxsgXEWbN++BUOwgPXgapcI2PcAYEGEdgGP8EQyzoLt27dgiPAKeBOtIuZqlwjY9wBgQYReAWcCLGCRhPb27VswBAuYBdwpoS5g3wOABREaBVwRYAGLJLS3b9+CIQIr4MWju5u8p3RGLK52iYB9DwAWRLgu4A0BFrBIQnv79i0YIrACHhWudomAfQ8AFkS4LOBGARawSEJ7+/YtGIIFrAdXu0TAvgcACyJcFXCrAAtYJKG9ffsWDMEC1oOrXSJg3wOABREuCrhTgAUsktDevn0LhmAB68HVLhGw7wHAgoihBbxVgAUsktDevn0LhmAB68HVLhGw7wHAgoghBSwSYAGLJLS3b9+CIVjAenC1SwTsewCwIGLXAhYLsIBFEtrbt2/BECxgPbjaJQL2PQBYELFLAfcSYAGLJLS3b9+CIVjAenC1SwTsewCwIKJvAfcWYAGLJLS3b9+CIVjAenC1SwTsewCwIKJPAe8kwAIWSWhv374FQ7CA9eBqlwjY9wBgQYS0gHcWYAGLJLS3b9+CIVjAenC1SwTsewCwIEJSwIMEWMAiCe3t27dgCBawHlztEgH7HgAsiOguYAcCLGCRhPb27VswBAtYD652iYB9DwAWRLQVsDMBFrBIQnv79i0YggWsB1e7RMC+BwALIpoK2KkAC1gkob19+xYMwQLWg6tdImDfA4AFEfUCdi7AAhZJaG/fvgVDsID14GqXCNj3AGBBxJea5RvDAhZJaG/fvgVDsID14GqXCNj3AGBBxJea5RvDAhZJaG/fvgVDsIAH8S0hkzNSnr9cMZIjEi7u0mwBFrAe/Oe2RMC+BwALIgCyAGCBcYaCBawHV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYaCBawHV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYaCBawHV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYaCBawHV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYaCBawHV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYaCBawHV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYaCBawHV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYaCBawHV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYaCBawHV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYaCBawHV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYaCBawHV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYaCBawHV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYaCBawHV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYaCBawHV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYaCBawHV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYaCBawHV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYaCBawHV7tEwL4HAAsiALIAYIFxhoIFrAdXu0TAvgcACyIAsgBggXGGggWsB1e7RMC+BwALIgCyAGCBcYaCBUwIIYRMAAuYEEIImQAWMCGEEDIBLGBCCCFkAljAhBBCyASwgAkhhJAJYAETQgghE8ACJoQQQiaABUwIIYRMAAuYEEIImQAWMCGEEDIBLGBCCCFkAljAhBBCyASwgAkhhJAJYAETQgghE8ACJoQQQiaABWyVf/+vh4c/+9Xvp34Yg/n+g1/8aerHMIAff/fB4eH/8bdTPwz7gOSZcSZ9YAEb5XeHCT/756kfyEB+/PjQ8sT684fp7+G/TP1ArAOSZ8aZ9IIFbJPvDn/2t6v18vHhX/zb1A9lGN8cWp5Yq3n7898vf/z/zBfH1KDkmXEmvWABm2S1Uv4u/vOHD9M/zfL9B6Yn1ndZYXxz+JdTPxTToOSZcSb9YAGb5IcPs1cKXxz+zcQPZRCrwfv/GP7QLO8NMhCQPDPOpCcsYNvYHlirh/+XlvdaKXqDOMJ2nhln0hMWsGl++ND0pzXfHf7iT5YnVvzY/9f/eXj48/829SMBwXaeGWfSFxawaWx/WJOMW+MT6zfpbqOWX7d5hOk8M86kNyxgy3xne3fF5P1GyxPru/iIjT8tf/yd7d+DN9jOM+NMesMCNsx3H/zM8k4T3yQ7jBqfWOlrhS8sv3LzBtt5ZpxJf1jAdvnG9j9Uv/8gefiWJ1ZmwbYJb7CdZ8aZ7AAL2CzW3yj65rDA6s6X33+QPfLiL2RnjOeZcSY7wAI2yo9fHP7c9iIBmFg/fpx1xneWT7/gBebzzDiTHWABG+ULlDVi+u2u/MMy24ev+gBKnhln0gcWsE2+AZlXxifW9x8c/ur3S+42OhiYPDPOpA8sYJP88GHxfpf1/RVNT6zldx+kF/ExvPeuD+DkmXEmfWABm+S7Q5SBZXxiLf/8m9XM+r/MX8Z2YnDyzDiTPrCACSGEkAlgARNCCCETwAImhBBCJoAFTAghhEwAC5gQQgiZABYwIYQQMgEsYEIIIWQCWMCEEELIBLCACSGEkAlgARNCCCETwAImhBBCJoAFTAghhEwAC5gQQgiZABYwWfP26buzKIoO7r2q3HwSRVdel74qfVH7sQq32u6//s7y4ii62ucRtorXmEeXnvfZLgGEcSa+wwImOW8erqfNjdJcOF9Nsb2Pii97T6yN+5e+2jqxXvzkdfnPXDy/uQ1OrOBhnIn/sIBJRjxY1uyvF/w82r9dmiu9J9bG/UuvILZNrFys9ufWVw6cWKHDOBMDsIBJSjKw3nkcD4KvHq7+XgySxXF0tTwAtkyL+Mc3bqjevzTN+k4s2WPgxAoexplYgAVMElZzJbr0LP/qzdH6fbXVKHtwFkUP8u/1nVib94/W79pxYhENGGdiAhYwSVjNlPIaX42ZfJKcrL6xGizFiOg7sTbvf+m/F+/acWIRDRhnYgIWMEk4Kf2jPubJjcfpX9KZMl+/hOg5sRruf+l5sevo5sRK3jCMDu7FGvPs07d58SlcIl58ubp39uZfMaFevrt6PXLzdfH1m3hzezfyF0PJl9E7j6VPC7EJ40xMwAImMau13/Jv7PTtttVLiPxzrp4Tq+H+yUuIdILVJ9biSXm3md4TK37nMebSw8zOPN/czeTHXjTslEPwYJyJDVjAJKb0Hl2Nk2Ttr0ZBPqd6TqyG+69uOMvetatPrNWEubm6/c3tbLeZ7t1GNybWSXL3ZOwlX682F79aePskHVkrl1dWXy4+ibrfKSTGYZyJDVjAJOasfDqBMvlImRfv6fWbWE33j4dJ9h5hbWIVX+afsvWbWMULk3mUf301/378EqV4J++En6lBwzgTG7CAScx6YuVve2VvquWfdq0Xf7+J1XT/bJjEf9Qm1ln+2dpqK9lrgD4TqxhE2VuQ631X0kc1L59BgeDCOBMbsIBJTNvEWr/XdrL+53aPidV4//zttKsdu42e7DCxStInta/Te5zFO7B8LntGiGEYZ2IDFjCJWf+LvjqxKucTSqdDr4nVeP9iB5MHjRPr7VefProW7TCxShvLvy4Rby/dJ+Zy7eTABA3GmdiABUxiykc2JmRvb83La77pGMZ8KhRvhlUnVuP909cOybt2GxPrxbXyhOk9sdbnC2ycWMlhHTH7PHIDGcaZ2IAFTBLqB06mE6t4/VCaSn0mVvP9szfv4nftahMr/fmDu79+tct7dg0vGRreEPzqUTIUH2x+h8DAOBMTsIBJQu3UQdnEKu9NelY7lKKFysRqvn8mFI+n/1wdKtlhG+ufkk6s+odk+dfNDzU+cGPLuYeIaRhnYgIWMEmIx0dpn8o3x8lXJ9VLrVWGSPuGypeKabp/PhrTT9Qa93Gpi7VOrKv5HSu7iWZfr+WTLdcnGoGFcSYmYAGTlOTNt/3H8f4cX//2dvoOW/WjtOxtvR4Tq+X+xbCYt06suehDs+Lnz2oHSq4PnMy2l57AaF46LIQvGaBhnIkFWMAk4/x2+QOuaP9Z/XwG2el+ytdJbfh3d3litdy/uFfyGVnDe3Zf3c4/YotnzOLV+s9sVK1vjs8FtHg6q5wqqHLqoHj/lPw9uviUgfdfJy+I+KEZOIwzMQALmOSsT1wbpQt7cVw50n+Rv48nnVht91/f63xWnVgX+dS88Uk6U86yFxX5n9nEyr/M94q58km20czC/j/UT56bvkJYH0bSfKIkggPjTPyHBUzWLF6+Gy/py9nR/eu3vFLm2QnkpROr7f6le81rp7FdPL2Wnl0g/zzsRTzTXhd/5u+jIxZ0AAAgAElEQVTd5TcnP3/5/vrjsq82Lh8T7yRaXC9m8fQgNniTR04GAONMfIcFTAghhEwAC5gQQgiZABYwIYQQMgEsYEIIIWQCWMCEEELIBLCACSGEkAlgARNCCCETwAImhBBCJoAFTAghhEwAC5gQQgiZABYwIYQQMgEsYEIIIWQCWMCEEELIBLCACSGEkAlgARNCCCETwAImhBBCJoAFTAghhEwAC5gQQgiZABYwIYQQMgEsYEIIIWQCWMCEEELIBLCACSGEkAlgARNCCCETwAImhBBCJoAFTAghhEwAC5gQQgiZABYwIYQQMgEsYEIIIWQCWMCEEELIBLCACSGEkAlgARNCCCETwAImhBBCJoAFTAghhEwAC5gQQgiZABYwIYQQMgEsYEIIIWQCWMADOYly9t553PYjV1471GvcWOPN57PoavHFfPUQb5W+c0v8wJw+fuI3Pue5tzSTSzyHBTyQ9cBa0bzcJxtYi+Po0vP136N1Ha/a+AELmGzic55ZwAQNFvBAKgPLydTYptdDYx7tfZT9dfWaNyrqOGtmFjCp43WeWcAEDBbwQNZrfPFJlLyu7PiRMR5GmbP1A1q95n1/ln91cVR6b5qQNV7nmY1KwGABD6Q8EuZRY69NN7DWRRu/5v0fR/mHwGfNk5UQr/PMAiZgsIAHUh4J57PG+TDdwFrVbnZzsj9W8UMn68+GCSnjdZ5ZwAQMFvBAyiNh9YIz/eLNw1kU7d14VvqRdL/jZfaqdPW/W4un16Lo8r383pX7rJrz6vLl7dUNN1fff/luFEXZdwq9r+Kfjw7uvS7fPK+9tC0+BE6+cZZ9lfdyeqf6Y9l4bM0/tkwf1+oBzut1Xrl9JfYgdnL5o7rJi+IVefqTqcLqB/bz3W+TH4/a9sYlCvid580Crj60WmKYXOI5LOCB1F4xJG/ZzfMDOf56/SPFMDuPP4ldffUfb6c/tJ+OgOp9Vmv/f8t2h7n0/En2nY9Keosn+X4y6f1bBtZ59rFvutdV/lU+PIv5VHksG4+t+cfS/arjB/iwOsZqt6++vBtlO4BVTTaMsZ8el3f+eVH1SEbA7zxvFHDtob1o2gqTS7yFBTyQ2mdmt9I/9j9f/ZP+djY90h/J3/ZNluxqxa7W8uvlm+Ps4NzafZJRsHqxEM+ly9GNV8keMaVXrfHPx68l3tzOPqZrGVj5h8DpKE1ehyzXr4uL+VR5LBuPrfnH4v1ls0dYHWO122MrK7k3jzdMNoyxKPH6JDUVvwP6LN0ZiLuMjYXfea4XcE2mnhgml3gOC3gg65Hw1cP0X/XFa4P8MNz0R86KUXQ1XbH5S9NsRlTus8hnRfyDVzOh0saKvavyO7Z9OFYdZMVba+kPr+dT7bFUvm75seI9yHl1jNVvj608qDzW3GTTGMu9xk9k8QYhP7IeD7/zXLu5LlNPDJNLPIcFPJDqcZPxKlwffJut6MqMSd8GLlZ0tkTr91mfQuNk/Slu8pd8+mW3VofiJmf5KEl+6iyfqLeybVeGZfZY6l+3/FgxZFbfKE+Z+u1rK3WTDWNsfderlaOYyVj4nefazXWZemKYXOI5LOCBVAbWzfQf7/mqzmbUSf6P+vXqXx8fNM9OiVG9z3r35eI7Z+WBVdbvLOB0PuYf5qWDozgIqfbqo+WxNf9Y/nb2svbP/I3bG6xk22oYY+u7ru5zFu/18nnXk0+c43eeazfXZeqJYXKJ57CAB7IeWJfTXSTXyzb/azY1kt7LVnltBW/cZz0MOgbW268+fXQt6i7g1YZulT5KO4k3W2xyvZNo6bFsfN38Y+sv168clhVn6ze8r5afjfVf24Wzh5jumHP53qvuXwFxiN95rt68+dBqiWFyieewgAeyMSlK/5AudldJfiRZpdmOyJsDq3qf7QPrxbV8UHYWcPKN8ntpl56v/7k+egFXTTYIP6huMjleJSod3UG08TvPGwVce2i1xDC5xHNYwANpGljNrxiSP9PPZAe/YkgPmDi4++tXW96CTj4EXl8V6Xy299F5cUbKgQWcD6X6GKvePuB1RMxXj5LJzBN3jYTfed72CjimlBgml3gOC3ggm5Oi7TOzZOSclD+MjWn9zKxzYGVHbay/31rAF0d7H60P5oh111NnSAH3+SStbmXjk7STzTFWslMcskL08TvPWz4DzskTw+QSz2EBD2RzUqzr7iwq7TWaHq6fvVdVH1j1+2wZWOt/kuf7X7YWcPwhcOmqhKuBeVLdYWTHAu6zL2mxC0zVZDE01wd3rF3dah2URBO/87yxF3RFZiMxTC7xHBbwQDYnRf2gwfWPnESXZ+l6bCi9yn3EA2u+7TPg1U/8h9Krg7PVQ6j9a33HAu5xNOXV5iemcHEW5cLF8VHp0ZTrY1P4OmIk/M7z1uOAq4lhconnsIAH0jApSqfNuVX5kbMoP15/o+Rq9xG+ZRf/eGVflvqZgzLR9Y3xZYGLYxQHFXD2GDbPJzTfOJ/Q+qO16hOz+vrKs2VyEt18jEU/fb1887A4Jdfe/ZXym2N+kjYafue5fJBU9kq7LFNPDJNLPIcFPJCmf6o3nTs35uKodGqd6kyo3WfbwLrIzm0b3fikPGqaCjgWXY+ZeG+X4gEPK+BFNg33/6E6xmq3l3chrT0xi/z8uZ/kwtdn6S2p0vksH7a3lmQc/M5zvYDrD62WGCaXeA4LeCCN75Wll065+ar+I8X02RxY1ftsPWwjubpLfLB/dceYhgKOh1Zp/5R5aSYMK2D5NWXW8tUnJnVx+X5JOH4NERXXtlk8PVh9efkmD6ccDb/zvFHAm4kqJ4bJJZ7DAh6TOeS52ds+f27/XLqZ0r6kxATW88zkkolhAY/I6p/UOJ8IFS9lKi8U2m/fDseYMYzmmckl3sACHpHzGdAxCcW7g+ezyvRpu307HGPGMJpnJpd4Awt4PN4cIy3T1ZTau796tfBiVv0gre327XCM2cJqnplc4g0s4LGY1w96sM5Zvqfn/jPR7VvhGLOE4TwzucQXWMBjcdZ/XXvO2yfxyW7feVzfX6Xt9m1wjFnCcp6ZXOIJLGBCCCFkAljAhBBCyASwgAkhhJAJYAETQgghE8ACJoQQQiaABUwIIYRMAAuYEEIImQAWMCGEEDIBLGBCCCFkAljAhBBCyASwgAkhhJAJYAETQgghE8ACJoQQQiaABUwIIYRMAAtYj2/VBdQV6EGyffsWRABkAcAC4wwFC1gPrnaJgH0PABZEAGQBwALjDAULWA+udomAfQ8AFkQAZAHAAuMMBQtYD652iYB9DwAWRABkAcAC4wwFC1gPrnaJgH0PABZEAGQBwALjDAULWA+udomAfQ8AFkQAZAHAAuMMBQtYD652iYB9DwAWRABkAcAC4wwFC1gPrnaJgH0PABZEAGQBwALjDAULWA+udomAfQ8AFkQAZAHAAuMMBQtYD652iYB9DwAWRABkAcAC4wwFC1gPrnaJgH0PABZEAGQBwALjDAULWA+udomAfQ8AFkQAZAHAAuMMBQtYD652iYB9DwAWRABkAcAC4wwFC1gPrnaJgH0PABZEAGQBwALjDAULWA+udomAfQ8AFkQAZAHAAuMMBQtYD652iYB9DwAWRABkAcAC4wwFC1gPrnaJgH0PABZEAGQBwALjDAULWA+udomAfQ8AFkQAZAHAAuMMBQtYD652iYB9DwAWRABkAcAC4wwFC1gPrnaJgH0PABZEAGQBwALjDAULWA+udomAfQ8AFkQAZAHAAuMMBQtYD652iYB9DwAWRABkAcAC4wwFC1gPrnaJgH0PABZEAGQBwALjDAULWA+udomAfQ8AFkQAZAHAAuMMBQtYD652iYB9DwAWRABkAcAC4wwFC1gPrnaJgH0PABZEAGQBwALjDAULWA+udomAfQ8AFkQAZAHAAuMMBQtYD652iYB9DwAWRABkAcAC4wwFC1gPrnaJgH0PABZEAGQBwALjDAULWA+udomAfQ8AFkQAZAHAAuMMBQtYD652iYB9DwAWRABkAcAC4wwFC1gPrnaJgH0PABZEAGQBwALjDAULWA+udomAfQ8AFkQAZAHAAuMMBQtYD652iYB9DwAWRABkAcAC4wwFC1gPrnaJgH0PABZEAGQBwALjDAULWA+udomAfQ8AFkQAZAHAAuMMBQtYD652iYB9DwAWRABkAcAC4wwFC1gPrnaJgH0PABZEAGQBwALjDAULWA+udomAfQ8AFkQAZAHAAuMMBQtYD652iYB9DwAWRABkAcAC4wwFC1gPrnaJgH0PABZEAGQBwALjDAULWI8vtQUQlgqABwALIgCyAGCBcYaCBawHC1giYN8DgAURAFkAsMA4Q8EC1uPLU2UBhKUC4AHAggiALABYYJyhYAHrwQKWCNj3AGBBBEAWACwwzlCwgPX48lS5gRGWCoAHAAsiALIAYIFxhoIFrAcLWCJg3wOABREAWQCwwDhDwQLWY1XAug2MsFQAPABYEAGQBQALjDMULGA9WMASAfseACyIAMgCgAXGGQoWsB5xAas2MMJSAfAAYEEEQBYALDDOULCA9WABSwTsewCwIAIgCwAWGGcoWMB6JAWs2cAISwXAA4AFEQBZALDAOEPBAtaDBSwRsO8BwIIIgCwAWGCcoWAB65EWsGIDIywVAA8AFkQAZAHAAuMMBQtYDxawRMC+BwALIgCyAGCBcYaCBaxHVsB6DYywVAA8AFgQAZAFAAuMMxQsYD1YwBIB+x4ALIgAyAKABcYZChawHnkBqzUwwlIB8ABgQQRAFgAsMM5QsID1YAFLBOx7ALAgAiALABYYZyhYwHoUBazVwAhLBcADgAURAFkAsMA4Q8EC1oMFLBGw7wHAggiALABYYJyhYAHrsS5gpQZGWCoAHgAsiADIAoAFxhkKFrAeLGCJgH0PABZEAGQBwALjDAULWI9SAes0MMJSAfAAYEEEQBYALDDOULCA9WABSwTsewCwIAIgCwAWGGcoWMB6lAtYpYERlgqABwALIgCyAGCBcYaCBaxHpYA1GhhhqQB4ALAgAiALABYYZyhYwHqwgCUC9j0AWBABkAUAC4wzFCxgPaoFrNDACEsFwAOABREAWQCwwDhDwQLWgwUsEbDvAcCCCIAsAFhgnKEIuIC/znmlJFArYPcNjLBUADwAWBABkAUAC4wzFIEW8JuH0ZpLz3VEWMASAfseACyIAMgCgAXGGYowC/jiKJqggJ03MMJSAfAAYEEEQBYALDDOUIRZwPMo2n//05zPXuuosIAlAvY9AFgQAZAFAAuMMxRBFvDiOLo6gsxGAbtuYISlAuABwIIIgCwAWGCcoQiygC+O9j4aQYYFLBGw7wHAggiALABYYJyhCLSAtT72rbBZwI4bGGGpAHgAsCACIAsAFhhnKIIs4MXxRK+AHTcwwlIB8ABgQQRAFgAsMM5QBFnAy3l0y82Gvu3iywY670DILrjJ8tY8EzIC7tJsgTALePUS+L6+StMrYKcvgRH+rQrgAcCCCIAsAFhgnKEIsoAXj+5E0d71uxnvjXUYEgu4QcC+BwALIgCyAGCBcYYiyAKunodjvBNxuG5ghKUC4AHAggiALABYYJyhYAGzgDsl1AXsewCwIAIgCwAWGGcogizgkWguYIcNjLBUADwAWBABkAUAC4wzFCxgPVjAEgH7HgAsiADIAoAFxhkKFrAeLQXsroERlgqABwALIgCyAGCBcYYi2AJ++/QgivYO7mldDHjJApYJ2PcAYEEEQBYALDDOUIRawPNiFyxHp+RooK2AnTUwwlIB8ABgQQRAFgAsMM5QBFrAcf9evn73zjXNBm4tYFcNjLBUADwAWBABkAUAC4wzFGEW8PksuvIs+dub40jtvNAsYImAfQ8AFkQAZAHAAuMMRZgFfBJdyc9+pXht4PYCdtTACEsFwAOABREAWQCwwDhDEWQBV66GdD67MuapKFnANQH7HgAsiADIAoAFxhmKIAu4cj1gvYsDdxSwmwZGWCoAHgAsiADIAoAFxhkKFvA0BeykgRGWCoAHAAsiALIAYIFxhiLIAl4cRw+KL86iCd6CZgEXAvY9AFgQAZAFAAuMMxRBFvD0O2G5aWCEpQLgAcCCCIAsAFhgnKEIs4DPZ9H+58nfvro9xWFILOC1gH0PABZEAGQBwALjDEWYBZyeCOvg4ED1VFjdBeyggRGWCoAHAAsiALIAYIFxhiLQAl6+mGVnoty7r6axpYCHNzDCUgHwAGBBBEAWACwwzlCEWsDLxcs7q1fA1x8r7YAVwwKWCNj3AGBBBEAWACwwzlAEW8AjsK2ABzcwwlIB8ABgQQRAFgAsMM5QsID1YAFLBOx7ALAgAiALABYYZygCK+DFo7vvvY7/X+a9KY4DdtHACEsFwAOABREAWQCwwDhDEVgBXxxFl57H/y8zyZmwXDQwwlIB8ABgQQRAFgAsMM5QsIBZwJ0S6gL2PQBYEAGQBQALjDMUgRXwqAgKeFgDIywVAA8AFkQAZAHAAuMMBQtYDxawRMC+BwALIgCyAGCBcYYiyAJePCrtd3V+5yeT7YQ1sIERlgqABwALIgCyAGCBcYYiyAL24XKELhoYYakAeACwIAIgCwAWGGcoWMDnMxZwh4S6gH0PABZEAGQBwALjDEVoBVzbATphkusBu2hghKUC4AHAggiALABYYJyhCK2Al2ebBfxASUpYwLs3MMJSAfAAYEEEQBYALDDOUARXwIt/uXv3zmzvenEerPc/15JiAUsE7HsAsCACIAsAFhhnKIIr4Bi9/a4qSAt45wZGWCoAHgAsiADIAoAFxhmKIAu4chiSHuIC3rWBEZYKgAcACyIAsgBggXGGIsgCHgkWsETAvgcACyIAsgBggXGGIuAC/jrnlZKAvIB3bGCEpQLgQd/CqRcTCyALABYA4swCXhNoAb956MfFGIY1MMJSAfDAAnYlwAKWSGhv374FQ4RZwNWjgVnAHRLqAvY9qFs4ZQG7UlAXYJwlEuoKZgizgOdRtP/+pzmfTXsijt0bGGGpAHjQtnDKAnamoC7AOEsk1BXMEGQBL46jqyPI9CrgXRoYYakAeGABuxJgAUsktLdv34Ihgizgi6O9j0aQYQFLBOx7ULZwygJ2p6AuwDhLJNQVzBBoAft1Io5dGxhhqQB4YAG7EmABSyS0t2/fgiGCLODFsYevgHdoYISlAuBB18IpC9ihgroA4yyRUFcwQ5AFvJxHt0ZQYQFLBOx7ULVwygJ2qaAuwDhLJNQVzBBmAa9eAt/XV+lbwL0bGGGpAHhgAbsSYAFLJLS3b9+CIYIs4MWjO1G0viCS1omhexdw3wZGWCoAHjQtnLKAnSqoCzDOEgl1BTMEWcDV83B4cSKOnRoYYakAeGABuxJgAUsktLdv34IhWMAs4E4JdQH7HhQt5LnwYmIBZAHAguU4FxLqCmYIsoBHYocC7tfACEsFwIOehSIWXkwsgCwAWDAc57WEuoIZWMB67FLAvRoYYakAeGABuxJgAUsktLdv34IhQi7ghdZ1CDNYwBIB+x7ULKxT4cXEAsgCgAW7cS5JqCuYIdgCfvlu/OHvxS/vKe0CvdyxgPs0MMJSAfDAAnYlwAKWSGhv374FQwRawIsn6d5XF0fRvtpZKXcr4B4NjLBUADxoWShlwouJBZAFAAtm41yWUFcwQ6AFfBJF+381u/R88fdRdEXrNfCOBSxvYISlAuBByUI5El5MLIAsAFiwGueKhLqCGcIs4LMoup9dkuHFLHqgpMIClgjY98ACdiXAApZIaG/fvgVDhFnAJ/G5oLNrIs3Vrg28awGLGxhhqQB40LFQSYQXEwsgCwAWjMa5KqGuYIYgCzi9GlJWwOczj07E0a+BEZYKgAcVC9VAeDGxALIAYMFmnGsS6gpmCLKA0+rNCljv4sAsYImAfQ8sYFcCLGCJhPb27VswBAvYxwIWNjDCUgHwoGGhlgcvJhZAFgAsmIxzXUJdwQxBFvDiON7xKmveM7XdoAcUsKyBEZYKgAcFC/U4eDGxALIAYMFinDck1BXMEGQBpztepQW8KmPvdsJiAbuV0N4+C9iRAAtYIqG9ffsWDBFmAZ/PopuvkwJ+czuKd8hSYUgBixoYYakAeHBvYSMNXkwsgCwAWDAY500JdQUzhFnAq5fAUXQw27t+bfXnLS2RQQUsaWCEpQLgwbmFzTB4MbEAsgBgwV6cGyTUFcwQaAEv/zDLrwas1r8DC1jQwAhLBcADC9iVAAtYIqG9ffsWDBFqAS/fPj1Yte/lG8/0JAYW8PYGRlgqAB5cW2iIghcTCyALABbMxblJQl3BDMEW8AiwgCUC9j04ttAUBS8mFkAWACxYi3OjhLqCGVjAegwt4K0NjLBUADywgF0JsIAlEtrbt2/BEMEW8B8/LfjMw+OARQ2MsFQAPLi10BgELyYWQBYALBiLc7OEuoIZAi3g9T5YyWWBdUSGF/CWBkZYKgAenFpozoEXEwsgCwAWbMW5RUJdwQxhFvBZFNko4O4GRlgqAB5YwK4EWMASCe3t27dgiCALeHEc7T3+uuCVkgwLWCJg34NLCy0x8GJiAWQBwIKpOLdJqCuYIcgCvjiKzwWtjosC7mxghKUC4MGhhbYUeDGxALIAYMFSnFsl1BXMEGgBa73rXMFJAXc1MMJSAfDAAnYlwAKWSGhv374FQwRZwItjQwXc0cAISwXAgzsLrRnwYmIBZAHAgqE4t0uoK5ghyAJezu28Bd3VwAhLBcCDMwv/f3vn4yVHdd35GiSdo2bISpxo8GJPRCQSZZF2nbDZZIJ2Y2OcDAFvsDngJRojAkiJm9FIMLT6/9+u6l/V3dVV91W9W3Xv930/59jSDMy78+26737o7uqq/S1gYmIB9AJABD/tXFNCvYIb0hTw5bHaLZBKUMCSAv4zUMCxClDAkhLa6/uP4Ig0BTx9fpxdvbvkbbMX4mgwMMJWAcgQK0JNB5iYWAC9ABDBTTvXlVCv4IZEBfyhl88B1xoYYasAZIgUoa4BTEwsgF4AiOClnWtLqFdwQ5oCPnNzIY5aAyNsFYAMFHCsAhSwpIT2+v4jOCJJARcX4lB63blERAFXGxhhqwBkiBOh9vCbmFgAvQAQwUk715dQr+CGJAXczzlYUQVcaWCErQKQIUqE+qNvYmIB9AJABB/t3FBCvYIbEhWwp88B7zcwwlYByBAjQsPBNzGxAHoBIIKLdm4qoV7BDUkK2NeFOCjgriW016eAIxWggCUltNf3H8ERSQp4epbd6qFKXAFXGBhhqwBkiBCh6dibmFgAvQAQwUM7N5ZQr+CGNAU8OTl4R79KZAHvGhhhqwBk6B6h8dCbmFgAvQAQwUE7N5dQr+CGJAU8eXgnyw5uuLkQxz4DI2wVgAwUcKwCFLCkhPb6/iM4IkkBXx57+xxwtYERtgpAhs4Rmg+8iYkF0AsAEey3s6CEegU3UMCeBLxlYIStApChawTBcTcxsQB6ASCC+XaWlFCv4IYkBdwTCgLeNDDCVgHI0DGC5LCbmFgAvQAQwXo7i0qoV3ADBawHBSwp4D8DBRyrAAUsKaG9vv8IjqCA9dAQ8IaBEbYKQIZuEURH3cTEAugFgAjG21lWQr2CGyhgPVQEXDYwwlYByNApguygm5hYAL0AEMF2OwtLqFdwAwWsh46ASwZG2CoAGbpEEB5zExMLoBcAIphuZ2kJ9QpuoID1UBLw2sAIWwUgAwUcqwAFLCmhvb7/CI6ggPXQEvDKwAhbBSBDhwjSI25iYgH0AkAEy+0sLqFewQ0UsB5qAl4aGGGrAGRoH0F8wE1MLIBeAIhguJ3lJdQruIEC1kNPwAsDI2wVgAytI8iPt4mJBdALABHstnNACfUKbqCA9VAU8NzACFsFIAMFHKsABSwpob2+/wiOoID10BRwYWCErQKQoW2EgKNtYmIB9AJABLPtHFJCvYIbEhPw5OHdXdzcDWmDKcZWAcjQMkLIwTYxsQB6ASCC1XYOKqFewQ2JCXjrNgy6N2PIMi35FmBsFYAM7SIEHWsTEwugFwAiGG3nsBLqFdxAAWsKWNnACFsFIAMFHKsABSwpob2+/wiOSEzAvfJI28AIWwUgQ6sIgUc6+i/dAoBeAIhgs52DsNHONqCA9Xg0VjbwI/+7PdWJFXikTUwsgF4AiGCyncOw0c42oID1eDRWNvCjR+oZOLEE64cXCD3SJiYWQC8ARLDYzmEYaWcbpCvgyTcLHv+50nvA+VnQqgZ+9Gjc/Ft0gxNLsD4FHKkABSwpob2+cgEr7WyDRAX8/L0eTsIqPoakLGBtA3NiCdYPLhB8pE1MLIBeAIhgsJ3DsNLONkhTwJsnQ1/TFLCmgXMBKxuYE0uwfmiB8CNtYmIB9AJABHvtHIaZdrZBmgI+y7KDG3dG+f+yg3e0qiwFrGbgQsC6BubEEqwfWKDFkTYxsQB6ASCCuXYOxEw72yBJAU9OsmtP8v+/n7v4mtKFsJZXwtIz8FzAqgbmxBKsH1agzZE2MbEAegEggrV2DsROO9sgSQFfHh/8apq799bs/09zDauwvBSlmoEXAtY0MCeWYP2gAq2OtImJBdALABGMtXMghtrZBokKuDjv6jy7vvp/DVbXgtYy8FLAigbmxBKsH1Kg3ZE2MbEAegEggq12DsVQO9sgbQHnrz5fHmu9Br2+GYOSgVcC1jMwJ5ZgfQo4UgEKWFJCe33FApba2QZJCnhyUrwEfTHKPbywcYmXv35wdPTzL8rf+s+/PTp6Y/NbzZTuhqRj4LWA1QzMiSVYP6BAyyPdLUKkhgboBYAIpto5lCjtDEWSAp6eFu/+zt8Knmu4xI/vHuX85N/W3/p98Z2jN/45qEr5doQqBn5UrhDhYamAE0uwvrxA2yPdKUKshgboBYAIlto5lCjtjEWaAr4YZTc/zU+DvpXLeOsl6M+PfvrF9IcPjn76x+V3nh298XfT/FvlGdbMxv2ANQy8IWAdA3NiCdYXF3dIzL8AACAASURBVGh9pDtFiNXQAL0AEMFQO4cSp52xSFPAM+vm1786z7KDUVY8G17z/YNiKv347urpwcsPjv5+Wnxr/qeUDT1qGHhTwCoG5sQSrC8t0P5Id4kQraEBegEggp12DiZKO4ORqICnf8hfeJ6cFhfC2nwC/PToZ4s//2bxnR/fXTxR+Hz1LRGbelQw8JaANQzMiSVYX1igw5HuEiFaQwP0AkAEM+0cTJx2BiNVAU+n/z7z7uTLw8N7W+dAf754VvBsMbc2/lEXAcc38LaAFQzMiSVYX1agy5HuEiFaQwP0AkAEK+0cTpx2BiNdAe/h5QeLV+q+f7B+z2xO6UW8Jd/V8WibLNv5VmxqfyEyJHqHNV5D9/NIkBSRtnPoxPZNmgI+vfnpvn9UM6+e7j6FkHbcAho4XRSPam2vBzV0Pw8FSRBxO9d2MxxJCvjyeP/lJ0vzausU0WddPoY0J/KL0DsvQee0eDxq4Gt2gvUlBbod6Q4R4jU0QC8ARLDRzuFEamc0EhXw/lsA733C8OzBG0HnQFcJOLKBKwUc18CcWIL1BQU6Huk4Au7Y0AC9ABDBRDuHE6ud0UhSwIsrYVWyb149DX3+WynguAauFnBUA3NiCdZvLtD1SCsIOLyhAXoBIIKFdg4nWjujkaSA83sQ7n0TuPqk0d+H+7dSwFENvEfAMRXMiSVYv7FA5yMd/yzoFg0N0AsAEQy0cwvitTMYaQr4xUdZduXG3QVvb3wQaflxyaelj2i8/PzozaCLYBVU6zGigfcKOJ6BObEE6zcV6H6ku30OOFJDA/QCQITh27kFEdsZjCQFfHmcldl8Q3j3wkHFxfz+uL1IM3v0GM/A+wUczcCcWIL1GwpEONJxr4TVsqEBegEgwuDt3IKY7QwGBbwt4JcfHL25dencp638u0/A8QxcI+BYCubEEqxfXyDGke4SIVpDA/QCQISh27kNMdsZjCQFXM8PpZvHfP9g9rRhcTeZnJ0PAtexV4+xDFwr4DgG5sQSrF9bIMqR7hQhVkMD9AJABIcCjtvOWFDAu/zw69lk+nnxFKGYV8+OIgs4loHrBRzFwJxYgvXrCsQ4zl0nVqSGBugFgAj+BBy7naGggPWo0WMcAzcIOIaCObEE69cUiHCUc0xMLIBeAIhAAUORpoBffFPmW6UqdXrMYii4UcDdDcyJJVh/f4HOh3iBiYkF0AsAEdwJ2Go72yBJAdeehBWPWj3GMHCzgDsbmBNLsP7eAl0P8AoTEwugFwAieBOw2Xa2AQU8kIBjvAwtEHBXBXNiCdbfV6Dj4S1hYmIB9AJABAoYiiQFPPnqkwX/eDs7+OVvnzT/SBua9NjZwCIBdzMwJ5Zg/T0Fuh3cDUxMLIBeAIjgTMB229kGSQq4zMXompJ/GwXc2cAyAXdSMCeWYP3qAp0O7RYmJhZALwBE8CVgw+1sg+QFPD3Lbimt3KzHjgaWCriDgTmxBOtXFuhyYHcwMbEAegEgAgUMBQWs9xRYoMduBhYLuL2CObEE61cV6HBYKzAxsQB6ASCCKwFbbmcbUMB1NwfuhkSPnQwcIOC2BubEEqxfUaD9Qa3ExMQC6AWACJ4EbLqdbUABX4yGFHAnA4cIuKWCObEE6+8WaH1I92BiYgH0AkAEChiK5AU8Oc0GfAl63MnAYQJuZWBOLMH6OwXaHtC9mJhYAL0AEMGRgG23sw2SFPDk4fJWwHfvjLIBT8IqaG/gQAG3UTAnlmD97QItD2cNJiYWQC8ARPAjYOPtbIMkBbx5IY7hPoa0oPVFsYIFHG5gTizB+psFWh3KBkxMLIBeAIhAAUORvICv3NPyr1jArQ0cLuBgBXNiCdbfKNDmQDZiYmIB9AJABDcCtt7ONkhSwD0RoMd2Bm4j4EADc2IJ1i8XaHFEBJiYWAC9ABDBi4DNt7MNKGA9QvTYysCtBBymYE4swfqlAq0OSDMmJhZALwBEoIChSFLAk4dvr193vrjzJ4OeBb2gjYFbCjjEwJxYgvXXBdodj2ZMTCyAXgCI4ETA9tvZBkkKeOPaG8NeiGNNCwO3FXCAgjmxBOsvC7Q9Gs2YmFgAvQAQwYeAHbSzDSjggS/EsSbcwO0FLDYwJ5Zg/UWB1gejGRMTC6AXACK4ELCHdrZBagLeuhWw6ueQQvUYbOAOApYqmBNLsP68QIdj0YiJiQXQCwARKGAoUhPw9HxXwPeVSgXrMdTAnQQsUzAnlmD9okCnQ9GEiYkF0AsAETwI2EU72yA5AU/+Jb/81cGN1bWw/vJ3WqXC9Rho4I4CliiYE0uwfl6g45FowMTEAugFgAgOBOyjnW2QnIBz9M672qCFHsMM3FnAzQbmxBKs/52yfo1MLIBeAIhAAUORpIA3PoakRxs9Bl0Uq7uAGxXMiSVY/7vuh6EBExMLoBcAItgXsJN2tkGSAu6JVnoMMXAMATcomBNLsH6Uw1CLiYkF0AsAEcwL2Es72yAxAZfug7RG6+lwy7ksN3AcAdcamBOrmUiHoQ4TEwugFwAiWBewm3a2QWICrvoUUmbkc8ArxAaONvn3Z+DEaiTeYdiPiYkF0AsAEShgKChgewIWGzji5N+XgROrgbiHYR8mJhZALwBEMC5gP+1sg8QE3Cvt57LQwFEnf3UGTqx6oh+GakxMLIBeAIhgW8CO2tkGFLAeHeayzMCRJ39VBk6sWjQOQxUmJhZALwBEMC1gT+1sAwpYjy5zWWTg6JN/NwMnVh1Kh2EXExMLoBcAIlDAUKQp4BfflPlWqUqnuSz5OJLC5N/OwIm1H8XDsI2JiQXQCwARLAvYVTvbIEkBb52KZe4krAKBgXUm/0YGTqy9KB+GDUxMLIBeAIhgWMC+2tkGFLBVAQtehtaa/KUMnFh76OEwlDAxsQB6ASCCXQE7a2cbJCngyVefLPjH29nBL39r60Ica5oMrDj5lxk4sarp6TAsMTGxAHoBIAIFDEWSAi5zMdK6HXB3ATcZWHXyzzNwYlXR42GYY2JiAfQCQASzAvbWzjZIXsDTs+yW0soR5nL9G8HKkz/PwIlVQb+HIcfExALoBYAIVgXsrp1tQAHrPQWOMZdrDaw/+TmxdhngMNiYWAC9ABDBqID9tbMNKGC9mwPHmcs1Bu5h8j/SeWjWuJtYgxwGExMLwF4AEShgKCjgi5FtAdcYuA8BjxtvGdwNZxNroMNgYmIB2Asggk0BO2xnGyQv4MlpZvkl6Jy9L0P3JGBVBfuaWEMdBhMTC8BeABFMCthjO9sgSQGX7gp8Z5RZPglrzj4D9yZgRQV7mlj7HiMKOFIBClhSQnv98AIu29kGSQp480Ichj+GtKLawD0KeKzlYD8Ta/9jRAFHKkABS0por08B90jyAr5yT8u/MQVc/SS4XwHrKNjNxKp5jCjgSAUoYEkJ7fWDC/hsZxskKeCeiDqXqwzct4DHCg52MrFqHyMKOFIBClhSQnv90AJO29kGFLAekefyroEHEHB0BbuYWA2PEQUcqQAFLCmhvX5gAa/tbAMKWI/Yc3nnSfAgAo6sYAcTq/ExooAjFaCAJSW01w8r4LadbZC4gL/RXDz6XN428EACHsd0sPmJJXiMKOBIBShgSQnt9YMK+G1nG6Qq4MnHr31WnI1181O1GgpzedPAwwl4HM3BxieW6DGigCMVoIAlJbTXDynguJ1tkKiAz0fFXYDz06EP7msV0ZjLG0+CBxXwOI6DTU8s4WNEAUcqQAFLSmivTwH3SJoCvhgtPv771Xuj7OBXSlVU5nLZwEMLeBzBwYYnlvgxooAjFaCAJSW01w8o4LmdbZCmgE+zq8tXnicn2XWlKkpzeW1gAwIed3Ww2YkV8BhRwJEKUMCSEtrrywu4bmcbJCngy+PSs17zN2PYYfUk2IaAx50cbHRiBT1GFHCkAhSwpIT2+uICvtvZBokKuORc67cjrGBpYDMCHrd3sMmJFfgYUcCRClDAkhLa60sLOG9nGyQqYNfPgMfLl6EtCXjc0sEGJ1bwY0QBRypAAUtKaK8vLOC9nW2QpICnp6X3fU+9vQdcUDwJNibgnOAHydrEavMYUcCRClDAkhLa68sKuG9nG6Qp4PMsu/lt8bcXH2aZ1ueQVOdybmCDAs4JepBsTax2jxEFHKkABSwpob0+BdwjaQp49rQ3yw4ODw9Hsz+1ngDrCrhQsE0B54gfJEMTq/VjRAFHKkABS0pory8q4L+dbZCogCcfLW9HePBXakW053LlLQoj0yWD6EGyMrG6PEYUcKQCFLCkhPb6kgIA7WyDRAU8U/DjO7NnwDd+oXY3YH0B92HgrhkaHyQTE6vjY0QBRypAAUtKaK8vKIDQzjZIVsA9oD6XHz1SV3CMDLUP0vATq/tjRAFHKkABS0por6/9H6xG2tkGFLAePQhY/UlwrAx7H6SBJ1aMbBRwrAIUsKSE9vqNBTDa2QbJCvjFx4f5eVj3vtUr0YeAtV+Hjpmh8kEacGLFykUBxypAAUtKaK/fVACknW2QqoDPlidhZbfUavQjYF0Dx86w8yANNLFiZqKAYxWggCUltNengHskUQHn/r1y4+6dVzUN3JOAVRWsk6H0IA0wsWKnoYBjFaCAJSW019c+Z8JIO9sgTQFfjLJr89shPT9xdjvCMqvJn6kpWDND8SD1PLE0clDAsQpQwJIS2uvXF4BpZxukKeDT+d2Ac/zdjnBNafJrGVg/Q38TSy0CBRypAAUsKaG9fm0BnHa2QZICnpxs3IzhmtJngfsUsJaC+8qgcwTm5Nu9hwiamJhYAPYCiDCsgIHa2QZJCtj77QiXbE5+ldehe85QEOvx7ysDBRyrAAUsKaG9fk0BpHa2AQUMI2AVBQ8h4DLtHvmtEoq/frE+BRypAAUsKaG9/v4CUO1sgyQFPDkp3QHpPMN4CboguoGHFnCZfY9zY4lOv2AzFHCsAhSwpIT2+nsLYLWzDZIUMOBJWCsiK9iSgFuX0F5f/64bJiYWgL0AIgwn4HgNbaKdbZCmgC9G2dXfFX/7+jbCx5DKxDUwBSxYX/++kya2KYC9ACIMJuCIHU0BrzCxs/unuBDW4eGh6qWwhpJXTAVTwIL1VQsUR9PExAKwF0CEoQQcs6dNtLMNEhXw9MvR8n7A76jVGExeEQ1MAQvW1ywwP5YmJhaAvQAiDCTgqE1top1tkKqAl/cDft/z/YD3T/5oCqaABevrFVgeRxMTC8BeABGGEXDctjbRzjZIVsA9MKS8YhmYAhasr1ZgdRRNTCwAewFEGETAkfvaRDvbgALWY1h5xVEwBSxYX6lA6QiamFgA9gKIMISAY3e2iXa2QWICnjy8+/aT/P/LvI3zOeAyUQxMAQvW1ylQPn4mJhaAvQAiDCDg6K1top1tkJiAL4+zVz7L/78MzJWwtomg4MEzxCihvb5Ggc1jZ2JiAdgLIEL/Ao7f3Cba2QYUMLCAI1ybcvgMEUporx+/wPaBMzGxAOwFEKF3AUdvbiPtbIPEBNwrFuTVVcEWMnQuob1+7AK7B83ExAKwF0CEvgUcubfttLMNKGA9bMirm4JtZOhYQnv9uAWqDpiJiQVgL4AIPQs4amubamcbUMB6WJFXFwVbydCphPb6MQtUHywTEwvAXgAR+hVwxM621s42SFzA32gubkde7RVsJ0OHEtrrxyuw70CZmFgA9gKI0KuAozW2wXa2QaoCnnz82mfFOVk3P1WrYUlebRVsKUPrEtrrxyqw/yCZmFgA9gKI0KeAI/W1zXa2QaICPh8V5z7np0Mf3G/+19thS17tFGwrQ8sS2utHKlBzgExMLAB7AUToUcBx2tpqO9sgTQFfjLL5HYG/em+EdjvC/bQxsLUMrUporx+lQO3RMTGxAOwFEKE/AcfoasPtbIM0BXyaXV2+8jw5ya4rVbEnr3AF28vQooT2+hEKNLw+YWJiAdgLIEJvAu7e1Lbb2QZJCvjyuPSs92KkdSGO71SbeNxm8ge/Dk0BC9bvXKDxsJiYWAD2AojQl4C79rT5drZBogIuOXfji6ioN3KbyR+oYApYsH7XAs2HxMTEArAXQISeBNyxpR20sw0SFXBfz4DnaPVxu8kfZGAKWLB+twKS42FiYgHYCyBCPwLu1NGNmGhnGyQp4Olp6X3fU7X3gNU/0N528gcomAIWrN+lgOwVCRMTC8BeABH6EHCHfhZhop1tkKaAz7Ps5rfF3158mGVan0NSv6h568kvfx2aAhas376A9ECYmFgA9gKI0IOAW7ezFBPtbIM0BTx72ptlB4eHh6PZn1pPgHd3Suw+7jr5BaOfAhasr//fQSYmFoC9ACLoC1h/R5poZxskKuDJR8t7ER78lVqRqjaL2sfdX/tsHP8UsGD9dgVCToczMbEA7AUQQT1DDzvSRDvbIFEBzxT8+M7sGfCNXzzRK1HdZhH7uOtWETiYAhas36ZA2NnoJiYWgL0AImhn6GNHmmhnGyQr4B7Y12bR+jjSB1DrPEABC9YPLxD6eWwTEwvAXgARdDO0a+dQTLSzDShgPfa3WaQ+jrJV6l1AAQvWb3NBsrArkpmYWAD2AoigmqFVO4djop1tkKyAX3x8mJ+Hde9bvRJ1bRaljyNtlTofUMCC9YMKSE9/28DExAKwF0AEzQwt2rkVJtrZBqkK+Gx5ElZ2S61GfZtF6OMe7oNHAQvWDyjQSr9GJhaAvQAiKGYIb+eWmGhnGyQq4Ny/V27cvfOqpoGb2qxzH/dwJ3gKWLC+tEBL+46NTCwAewFE0MsQ2s7tMdHONkhTwBej7Nr8dkjPT/RuR9jcZh37OOpWqZYDBSxYX1agvX6NTCwAewFE0MoQ2s5dMNHONkhTwKfzuwHnKN6OUNJmnfo48lapEgQFLFhfUqCDfcdGJhaAvQAiKGUIbOdumGhnGyQp4MnJxs0Yril9FljYZu37OPpW2bUEBSxYv7FAlye/BSYmFoC9ACLoZAhq586YaGcbJCngXm9HKKBtHytslW1VUMCC9RsKdNavkYkFYC+ACBoZgto5Aiba2QYUsAEBT1s6WGWrbOqCAhasX1cggn3HRiYWgL0AIihkCGjnOJhoZxskKeDJSekOSOfZwC9BL2jRx1pbpSQNCliw/v4CcfRrZGIB2AsgQvwM8naOhYl2tkGSAjZ0ElaZ4D7W2yorcVDAgvX3FYhk37GRiQVgL4AIsTPI2zkeJtrZBmkK+GKUXf1d8bevbw/5MaQdwvpYdavMHUwBC9avLBDryW+BiYkFYC+ACJEzSNs5Kiba2QZpCnh+IazDw0PVS2G1arOQPlbeKllUh+wBVMCRHzoTEwvAXgAR4mYQtnNkTLSzDRIV8PTL0UIvB++o1WjbZuI+1t8q+g5GFHD8B83ExAKwF0CEmBlk7RwfE+1sg1QFvLwf8Pu93w9YgrCPe5GXsoPxBKzxeJmYWAD2AogQMYOonTUw0c42SFbAcfhOi0eWWDh46F/DA0qPVH2zOOhnYo/oXRqnneN1swcoYD26tZLgPyR7fPao9jwY6Rmw3osFJqYSwNNHgAixMjS3sx4m2tkGaQt48tUnv9VbvWubNfZxv/LS0QuMgFVfqjcxsQDsBRAhUoamdlbFRDvbIFUBP//fT6bTy9uziXlV61NIEXZKQx/3Li8FyWAIWPtkNRMTC8BeABHiZKhvZ21MtLMNEhXwWZZff/K0mJpaV6KMslNq+3gIecVWDYCAe/i4lomJBWAvgAgxMtT2GgXcJ2kK+LzQbn5X4CfPj9U+CKz9WtFQ8ooqHO8C7uXD0jYmFoC9ACKov7BGAfdJmgKeX4ryvLgIlpVrQe9nyK2yp0I87fgWcE+XC7MxsQDsBRChe4aGXqOA+yRJAS/uBzzXsI27ITUw2FZpus9AZwc7FvDyAUhlYgHYCyBC1wyNvZZKO9sgSQHPnXt5XNyGwYWAq/fN0PKK4eChM7QkK2VPZWIB2AsggvqnG1NpZxskLOCLUXFTQicCng5z35KmCp0dbCBDKNlW6FQmFoC9ACJ0yiDptVTa2QZJCnj+EvTZ/D5I9t8DXtP/VhFU6OZgGxnEbMu3WD+RiQVgL4AI6le4TaWdbZCkgKen2fX89OfcvObPgt6k760iq9DBwWYyNFMl32L9RCYWgL0AIrTPIOy1VNrZBmkK+Hw+Se9PJ+9lpu4HLKDfrSKu0NbBljLUsE++xfqJTCwAewFEaJtB3GuptLMN0hTw/H7A14sTsQ7uaxXRarM+t0pIhTpJ7S9gK0MVTblSmVgA9gKIoH6b8VTa2QaJCnj6/OHb78/+uPyzm5+q1dBrs/62SmiFYAkbzFBGkieViQVgL4AIbTIE9Voq7WyDVAXcB5pt1tdWaVMhywI0bDRDjjRFKhMLwF4AEVpkCOu1VNrZBhSwHrpt1s9W6SqvRoEZzRDyTD6ViQVgL4AIwRlCey2VdrYBBayHdptZldcagYTNZQh6/j5fP5GJBWAvgAihGYJ7LZV2tgEFrIf+brcmr2pqhWZIwOJn7dvrJzKxAOwFECEsQ4teS6WdbUAB69HHbtfeKtqfobUi4HbqXayfyMQCsBdAhKAMbXotlXa2AQWsRz+7XXeraFxFakNyBgTcXr2L9ROZWAD2AogQkKFdr6XSzjaggPXoa7drbhWt6yivhDewgLvKt1g/kYkFYC+ACPIMLXstlXa2AQWsR3+7XW+r6GzGbAeVMgu2M8SuncrEArAXQARphta9lko724AC1qPP3a61VTQ3Y18aXmfQqZjKxAKwF0AEWYYOvZZKO9uAAtaj592uslXUN+Niu2tq+JH2+olMLAB7AUSQZOjUa6m0sw2SFPDln73+bQ9let/tClulLwHP2X2GGirO7it0jKCDiYkFYC+ACM0ZOvZaKu1sgzQFfJxlV+4p3QV4zRC7PfZW6VfAc+ol2o6eI8TGxMQCsBdAhKYMnXstlXa2QZICnnxYDOWr7+s6eJjdHnerDCHgfbQVrXaGVCYWgL0AItRniNBr6u2cZSba2QZJCnjG128V8/q19xVrDLXbY24WSwJuXUJ7fQo4UgEKWFJi/z+K0mvK7VzMXfUHyQ0JPxSP5w7Wux/hcLs93nahgAXrU8CRClDAkhL7/kGkXlNt5/nLVCba2QYJC3g6nTy+nffDwT2dU7KG3O2xNgwFLFifAo5UgAKWlKj+drReU2zn5btEJtrZBkkLeMaL90bFS9EaT4OH3e1xtgwFLFifAo5UgAKWlKj6ZsReU2vn9UkaJtrZBkkLePLxq6uzd16Pv/zQuz3GpqGABetTwJEKUMCSErvfitprSu1cPkfSRDvbIF0BL15/Ls6FfvFhlt2KXmH43d5921DAgvUp4EgFKGBJie1vRO41lXbe/IiCiXa2QaICXtp39Wng8+xa9M8kWdjtXTcOBSxYnwKOVIAClpTY/DJ6rym08/YnBE20sw2SFPDkH7Ltk68uj1/5LHYZI7u909ahgAXrU8CRClDAkhLlLxR6LXo7735A30Q72yBJAedXwjrY/PjR5THmM+CCDpuHAhasTwFHKkABS0qs/6rSa5Hbuer6OCba2QaJCvjm77a+Nflt/I8iWdrtbbcPBSxYnwKOVIAClpRY/kWp16K2c/Xl6Uy0sw2SFHBP2Nrt7TYQBSxYnwKOVIAClpSY/6HWaxHbed/VYU20sw2SFPDk4dvr15sv7vyJ0iWhre32NluIAhasTwFHKkABS0pMFe07jtjO+++CYqKdbZCkgDfOuNI4/WqOwd0evIkoYMH6FHCkAhSwpISqfqO1c91NyEy0sw0o4ItRQgIOVjAFLFifAo5UgAIW4KKd6+8BaqKdbZCagPMToLeJf/7zHKu7PWQjUcCC9SngSAUo4EZctHPTLbhNtLMNUhPw9HxXwPeVStnd7QG7sfNmbCpAAQswMbHs9rO8gnoB3QpFuyn3Wud2btKvkXa2QXICnvzL3bt3Rgc37i75y+1PJEXD8m4X78aOm7G5AAUswMTEstzP0grqBTQrLNpNudc6tnOzfo20sw2SE3CO3nlXGxjf7bLd2GkzSgpQwAJMTCzj/SyqoF5Ar8Kq3ZR7rVM7S/RrpJ1tkKSANz6GpIf53S7ZjR02owgKWIKJiWW+nwUV1AsoVSi3m3KvdWhnmX6NtLMNkhRwT3jY7Y27sfVmFEIBSzAxsTz0c1MF9QIaFbbaTbnXWrezVL9G2tkGiQl48vDu7Mnv7P/LaD0d9rHbG3Zjy80ohgKWYGJi+ejn+grqBaJX2G035V5r2c5y/RppZxskJuDL4+yVz7Y/i5TU54ArqN2NrTZjABSwBBMTy0s/11VQLxC3QmW7Kfdaq3YO0a+RdrYBBUwBT2scTAEL1qeAIxWggDfY027KvdaincP0a6SdbZCYgHvF1W7ftxuDN2MgFLAEExPLVT/vqaBeIF6Fve2m3GvB7RyqXyPtbAMKWA9Puz2ncjcGbsZgKGAJJiaWt36uqqBeIFKFunZT7rXAdg7Xr5F2tgEFrIeb3b6iYjcGbq1gKGAJJiaWv37eraBeIEqF+nZT7rWgdm6jXyPtbIPEBLx1/jPPgt5mZzcGb65AKGAJJiaWx37erqBewP8nAwPauZ1+jbSzDRITcNW9GHgS1iabu7HF9gqCApZgYmI57eeNCuoF/F8bR9zObfVrpJ1tQAFTwLuUdmOrDRYABSzBxMTy28/rCuoFulSQ9YKRdm6vXyPtbIPEBNwrtnd7A8vd2HKLiaGAJZiYWK77eVFBvUDrCuJeMNHOXfRrpJ1tQAHrYXi3iyh2Y+tNJoQClmBiYnnv56ndCCG9MHw7Z930a6SdbUAB62F1twdAAUvWp4AjFUhUwGG9MHQ7d9avkXa2QWIC5rWgQ0sETodgKGAJJiYWQD9bjBDaC4O2cwT7jo20sw0SEzAvRRlaIv+/rvutDgpYgomJBdDP5iK06IUB2zmOfo20sw0oYAq4tsTiz+6bbg8UsAQTEwugn41FaNULg7VzJPuOjbSzDRITLSesewAAIABJREFUcK8Y2+3tSqz/GmXr7UABSzAxsQD62VKEtr0wTDvHevJbYKKdbUAB62Fpt7cusfFVpP1XhgKWYGJiAfSzmQgdemGIdo6qXyPtbIOUBTz5Rnd9M7u9S4ntb0TbhAsoYAkmJhZAP9uI0K0Xem/nyPYdG2lnGyQr4MdvFW1181O9EjZ2e8cSFd+LuRUpYBEmJhZAPxuI0LkXem7n+Po10s42SFTAk5PVKVivK30IycRu715iz/ej7UUKWIKJiQXQz0NHiNELvbazgn3HRtrZBmkKOPfvwY1ffPKbO7P2uq5VZejdHqVEzT+LshcpYAkmJhZAPw8aIVIv9NfOGk9+C0y0sw3SFPB5lt2a/23yUZbdV6oCMLAaM3TeixSwBBMTC6Cfh4sQrxf6amc1/RppZxskKeDZE+Bbqy9O1Z4CAwwsSYZue5EClmBiYgH080ARovZCL+2saN+xkXa2QZICvjw++NXqi4sRL8RRU0L0b3XYixSwBBMTC6Cfh4gQuxd6aGdd/RppZxskKuCScze+iArAwJJnaLsXKWAJJiYWQD/3HkGhF9TbWdm+YyPtbIMkBTw52XgGfI03Y9hfIuDfbbUXKWAJJiYWQD/3G0GnF3S7TfvJb4GJdrZBkgKenpXe9z0rvR8cF4CB1SJD4F6kgCWYmFgA/dxjBLVe0Oy2XvRrpJ1tkKaAL4+zP1087T1TuxcDwsBqnUG8FylgCSYmFkA/9xVBsxfUum1h31Ta2QaJCXh5J+A7888B/+vdUZbd4P2Aa0p0+FnRXqSAJZiYWAD93EcE7V5Q6rbVk99U2tkGiQl460aEvB1hY4mOP9+4FylgCSYmFkA/axdw286ll55TaWcbUMAUcG2JCGvU7kWvE6u8fiITC6CfNQv01QvxK2y+85tKO9sgMQH3CsDAiphhz150ObG21k9kYgH0s1qBHnshdoXtE69SaWcbUMB6AAys6Bl29qLDibWzfiITC6CfdQr02wtRK1Sc9pxKO9uAAtYDYGBxYknWT2RiAfSzQoHeeyFihcpPHaXSzjZIV8CTbxY8/nO+B7y/hOLafW33mBUqHyP163XamFgA/cwXdErs+cwvBdwniQr4+Xs8CUtWQr2AzQ9OtojQFsmvY2JiAfRz1AKVveZFwPsvuUEB90maAt48GfoaBby/hHqBYa9erxShLaYnFkA/85zCOXVXvKKA+yRNAZ/l1+G4M8r/lx28o1UFYGCZytB2u+cDRTEBxGGQYKkX2laIs0xNrzkQcMP1JingPklSwJOT7NqT/P/v5y7WuhcDwsBiBsn6/iOIAOgFfqy9+XLPFHCfJCngxf2A57dhOM01rALAwGIGyfr+I4gA6IXOBZrUYlzAkpstUMB9kqiAi/Ouzot7Ip2X7owUF4CBxQyS9f1HEAHQC+qny1kWcCa71xEF3CdpCzh/9fnymPcDrimhXsB/BoAIIgB6oUMBmVrMClho3zEF3C9JCnhyUrwEfTHKPbywsQIAA4sZJOv7jyACoBfaFhCrxaaA5fYdU8D9kqSAp6fFu7/zt4LnGtYAYGAxg2R9/xFEAPRCmwJBarEo4BD7jingfklTwBej7Oan+WnQt3IZ8yXomhLqBfxnAIggAqAXQgsEq8WcgAPtO6aA+yVNAc+sm1//6jzLDkZZ8WxYA4CBxQyS9f1HEAHQCyEFWqnFmIDD9UsB90uiAp7+IX/heXJaXAiLnwOuKaFewH8GgAgiAHpBWqC1WiwJuI19xxRwv6Qq4On032fenXx5eHhPy78IA4sZJOv7jyACoBckBTqpxY6AW+qXAu6XdAWsD8DAYgbJ+v4jiADohaYCndViRMCt7TumgPslZQFPvtFdH2BgMYNkff8RRAD0Qm2BGGqxIOCgDx1VrE8B90iyAn78VtGlNz/VKwEwsJhBsr7/CCIAemF/gUhqGVzAHe07poD7JVEBT05WNyN8ne8B15VQL+A/A0AEEQC9sKdAPLUMK+Du9h1TwP2SpoBz/x7c+MUnv7kza1etS0EjDCxmkKzvP4IIgF6oKhBVLQMKOIp9xxRwv6Qp4PPVh38nH2W8G1JdCfUC/jMARBAB0AvbBaKrZSgBx7LvmALulyQFPL8E1oJT3g2proR6Af8ZACKIAOiFcgEVtQwi4Ij2HVPA/ZKkgBf3A57Da0HXllAv4D8DQAQRAL2wLKCmlv4FHNe+Ywq4XxIVcMm5vBtSbQn1Av4zAEQQMRvMugV6ErCmWnoWcHT7jingfklSwIvbEc65GPFmDDUl1Av4zwAQQcR8MCsW6EHA2mrpU8Aa9h1TwP2SpICnZ6X3fc94M4a6EuoF/GcAiCBiNZi1Cig/kOO2N7MPoD8B69h3TAH3S5oCvjzO/nTxtPcs03oFmvISFfCfASCCiPJgVimg+EAu5aKtlp4ErGbfMQXcL4kJePLwbsGd+eeA//XuKMtuvM2XoPeXUC/gPwNABBFbgzl+AaUHsiwXbbX0IWCll55X61PAPZKYgGdPfXfhSVg1JdQL+M8AEEHE7mCOXEDhgdyWi7Za1O2lbN8xBdwvFDAFXFtCvYD/DAARRFQO5pgFoj6QlZPfuYD17TumgPslMQH3CuUlKeA/A0AEEfsGc7QCkR7ImsnvWcBL+2pnoID7hALWg/KSFPCfASCCiLrBHKVAhAeyYfK7FXDpuS8FjAQFrAflJSngPwNABBFNg7lzgY4PpGDy+xTw5ivPFDASyQr4xceHWXZweO9bvRKUl6SA/wwAEURIBnOnAh0eSOHkdyjgnfd9KWAkUhXw2eoULK3LcFBesgL+MwBEECEfzC0LBD6QLSa/NwFXnXVFASORqIBz/165cffOq5oGprwkBfxnAIggInQwBxcQP5CtJ78rAe8555kCRiJNAV+MsmufFn97fpKVrgs95+WvHxwd/fyLre/++O7PAqtQXpIC/jOYjxCpobsMZlEBwQPZcfL7EfD+TxxRwEikKeDTbHUDhsnJ9v2Af3z3KOcn/7b57c+PKGCNAv4zWI8Qq6G7D+aGAnseyAgTf4kTAdd+3pcCRiJJAdffDenzo59+Mf3hg6Of/rH0zZefH1HAKgX8Z7AeIVZDO7FXbQX1At0rNF1tgwJGIkkB194P+PsHxVOFH99945/X3/zP/3lEAesU8J/BeIRoDe3BXk0V1At0q5A12XdMAWNBAW8L+OliLj09+pvS947++j8oYJUC/jMYjxCtoa3bS1JBvUD7ChL5FiVaV5BBAfdJkgKenGT3V1+cZ5svQX9+9PfFn89K8+npm/+08bUMyktSwH8G4xGiNbRle0krqBdoV0Eq36JEqwpyKOA+SVLAdSdhvfxg8Urd9w823jOrnlffETI4tb0e1NC1ZR4RFRbyHfrX6JH23QxHmgK+GGVXf1f87evbWx9DooCJN2p7nQI2TXr2fUQBl0hTwPMLYR0eHu5eCqs0rzY/t8GXoHUK+M9gO0K8hrb6+m1IBfUCQRVa3V6QL0EjkaiAp1+OFs1/8M7mPwh7BlwL5SUp4D+D7QjxGtqYvVpVUC8gr9D25r4UMBKpCng6eXxn9gz4xvtPtr5PAW+WUC/gP4PtCBRwuYJ6AVmFkJOudkq0+aGQ9SngHklWwHupOmm06utmKC9JAf8ZjEeI1tBW7NWlgnqB5gpd5FuUaPuD0vUp4B5JU8CnNz/d+8+WH5csf2wyhwLWKeA/g/EI0Rragr26VlAvUF+hq3yLEl1+WLI+BdwjSQr48rj0OeBtKi8cNKWAtQr4z2A8QrSGHtpeMSqoF6ipEEG+RYmuCzStTwH3SKIC3rz41QYvPzh6c/fSuRSwVgH/GYxHiNbQFLCgwL4Kkew7poCxSFLAGzdj2OGH0s1jvn+wetpAAesU8J/BeoRYDU0BCwpUVohn3zEFjEWSAp6eLW8HXMkPv56Nq58XTxcoYPUC/jOYjxCpoSlgQYHdClHtO6aAsUhTwC8+yrIrN+4ueHv7o0iRoLwkBfxnAIggggIWFNisEOOkq50SMRerWp8C7pEkBXx5nJWpeUO4E5SXpID/DAARRFDAggLrChryLUrEXnB7fQq4RyhgCri2hHoB/xkAIoiggAUF5hW05FuU0Fi0vD4F3CNJCrgnKC9JAf8ZACKIoIAFBR6pyrcoobXwcn0KuEcoYD0oL0kB/xkAIoiggBvRlm8OBYxE0gL+Rnd5yktSwH8GgAgiKOBa+pBvDgWMRLICfvxWcS+kmmtSdobykhTwnwEggggKeC8r+fZgLwoYiUQFPDlZnYL1utKHkCgvWQH/GQAiiKCAK9l45ksBSzDRzjZIU8C5fw9u/PKT39wZZdl1rSqUl6SA/wwAEURQwDvsvOxMAUsw0c42SFPAZ1n2p/MnvpOPspobM3SD8pIU8J8BIIIICniDyvd8KWAJJtrZBkkKePYE+Nbqi1O1p8CUl6SA/wwAEURQwCv2nnCFLWBJl0hKmGhnGyQp4Mvj0s0YLka8EEdNCfUC/jMARBBBARfUnu2MJ+AuHbOvhIl2tkGiAi45t/behJ2gvCQF/GcAiCCCAm7+qBGGgDW6p1zCRDvbIEkBb9yO8GJ0jTdj2F9CvYD/DAARRCQuYNHnfP0KeHWYU2lnGyQp4OlZ6X3fM74HXFdCvYD/DAARRCQsYPFFNjwKeOswp9LONkhTwJOT1cd/z9TuxUB5iQr4zwAQQUSiAg66wpUvAVce5lTa2QZJCnjyMP/87417n/xr/udrWncFprwkBfxnAIggIkEBB19e0ouAaw5zKu1sgyQFvHU7Qq2bElJekgL+MwBEEJGYgFtd29m+gBsPcyrtbAMKmAKuLaFewH8GgAgikhHweiiEFzAsYOFhTqWdbZCkgHuC8pIU8J8BIIKIBARc/i/ydgVMCjjoMKfSzjaggPWgvCQF/GcAiCACXMAd1bsoYE7AwYc5lXa2AQWsB+UlKeA/A0AEEcACjqDeRQFTAm51mFNpZxtQwHpQXpIC/jMARBABKuBo8i0KmBFw68OcSjvbgALWg/KSFPCfASCCCDgBR3nReauACQF3OsyptLMNKGA9KC9JAf8ZACKIABJw55Ot9hYYXMCdD3Mq7WwDClgPyktSwH8GgAgiUASso95FgUEFHOUwp9LONqCA9aC8JAX8ZwCIIAJBwHrqXRQYSMARD3Mq7WwDClgPyktSwH8GgAgivAtY8Ynviv4FHP0wp9LONqCA9aC8JAX8ZwCIIMKvgJXe8K2gZwFrHOZU2tkGFLAelJekgP8MABFEeBTw1tlWHiPslFj8qXWYU2lnG1DAelBekgL+MwBEEOHLXpUnOvuKsKfEWE++Oam0sw2SF/Dkq09+G/1GhHMoL0kB/xkAIojwYq+azxh5iVAH2xmJ5AV8eaxwH6Q5lJekgP8MABFEeLBXw7u9HiLUkR8GtjMSFDAFXFtCvYD/DAARRFi3l+BEK+sR6lgeBrYzEskLmC9B15dQL+A/A0AEEZbtJTzL2XKEOsqHge2MRPICVoTykhTwnwEgggib9gr6iJHNCPVsHwa2MxIUsB6Ul6SA/wwAEURYs1eLCzpbi9BE1WFgOyNBAetBeUkK+M8AEEGEHXu1vpeCnQjN7DsMbGckUhPwi292+VapFuUlKeA/A0AEERbs1fE2RhYiSKg7DGxnJBIT8OVxtgvPgq4poV7AfwaACCKGtVdH9S4qtP5JaYHuFZoOA9sZCQqYAq4toV7AfwaACCKGslcU9S4qdPz55gLdKkgOA9sZicQEPPnqk4KPsuzg7V9+8sk/vpod3OPHkGpKqBfwnwEggoj+7RVRvYsKUVapK9C+gvQwsJ2RSEzAC2ZPhG8t/vql2hNgyktUwH8GgAgi+rRXdPUuKkRcq7pAuwohh4HtjESaAj5d+Xc6PcuuK1WhvCQF/GcAiCCiH3spqXdRIfqK2wXCK4QeBrYzEkkKeHJy8KvVFxeja3wJen8J9QL+MwBEEKFtL1X1zrEm4DaHge2MRJICvjwuvey88UVUKC9JAf8ZACKI0LDX7imRCkXWWBJw28PAdkYiUQFvPAOmgGtKqBfwnwEggoi49qpQr/69/KwIuMthYDsjkaSAp6el931P+R5wXQn1Av4zAEQQEctee5/vpiHgroeB7YxEmgI+z7LX52/8Tj7MsvtKVSgvSQH/GQAiiIh5t97Kl5rxBRzjMLCdkUhTwLOnvVn22t27d1+d/fm6VhHKS1LAfwaACCK62Ev0Li+2gGMdBrYzEokKOH/iu+BW87/dEspLUsB/BoAIIkLtFXyCFa6AYx4GtjMSiQp4On3xcf7s98o9rTsxTCkvWQH/GQAiiJDbq+W5zZgCjn0Y2M5IJCvgHqC8JAX8ZwCIIEJir04fKEITsM5hYDsjkbKAJ4rPfnMoL0kB/xkAIojYZ69on+XFEbDmYWA7I5GsgB+/ld8G6fLP7ildBmtKeckK+M8AEEHErr2iqXfDXor0IWDtw8B2RiJRAc9PwpoJ+Di7qnUvBspLVMB/BoAIIsr2iibdLXspo10AoRcAIjgiUQGfZtnVvxi98tnkH7JM61LQlJeogP8MABFERH6+u4tvARePEUAvAERwRJoCPs+ydxYXgf5yxAtx1JVQL+A/A0AEEYrqneNVwKXHCKAXACI4Ik0BF7cjXNyFgbcjrC2hXsB/BoAIIoa+jFSMCpHX23mMAHoBIIIjkhTw/HaECwHzZgy1JdQL+M8AEEEEBVxiz2ME0AsAERyRpIDn6l0ImLcjrC2hXsB/BoAIIijggtrHCKAXACI4ggKmgGtLqBfwnwEggggKuPkjRgC9ABDBEUkKeHKSn3i1MO+52mnQlJekgP8MABFEJC5g0WME0AsAERyRpIDnJ17NBTyTMU/CqimhXsB/BoAIIhIWsPgxAugFgAiOSFPAF6Ps9SeFgJ/fzvITslSgvCQF/GcAiCAiSQEHPkYAvQAQwRFpCnj2FDjLDkcHN17VvB8h5SUp4D8DQAQRiQm41WME0AsAERyRqICnfxjxfsCiEuoF/GcAiCAiGQF3eIwAegEggiNSFfD0xceH+f2Ab36qV4LykhTwnwEggogEBNz5MQLoBYAIjkhWwD1AeUkK+M8AEEEEtIAjPUYAvQAQwRFpCvhU84nvCspLUsB/BoAIIkAFHPUxAugFgAiOSFLAl8dqN2AoQ3lJCvjPABBBBJyAFR4jgF4AiOCIRAWsdfGrDSgvSQH/GQAiiAASsNpjBNALABEckaSA5zdjUIfykhTwnwEggggMAes+RgC9ABDBEUkKeHqWXevhTWDKS1LAfwaACCK8C3jKdhat7z+CI9IU8IuPsuzKjbsL3ua1oPeXUC/gPwNABBF+BbyKwHYWrO8/giOSFPDlcVaGd0OqKaFewH8GgAgiPAp4KwLbWbC+/wiOoIAp4NoS6gX8ZwCIIMKXgCsjsJ0F6/uP4IgkBdwT3O2SAv4zAEQQ4UXANRHYzoL1/UdwBAWsB3e7pID/DAARRNgXcGMEtrNgff8RHJGmgL/tpQp3u6SA/wwAEURYFrAwAttZsL7/CI5IT8CTD/MbIV15R+nU5xLc7ZIC/jMARBBhU8BBEdjOgvX9R3BEcgK+WN6H8Kr6xbC42yUF/GcAiCDCmoBbRGA7C9b3H8ERqQm4OAH6yn95dfb/17SfA3O3Swr4zwAQQYQdAbeOwHYWrO8/giNSE/BZlhWXoXw8eyKsfUMG7nZJAf8ZACKIsCDgjhHYzoL1/UdwRGICnpwsvTsz8XXlYtztkgL+MwBEEDGsgKNEYDsL1vcfwRHpCXhxH4aLkfpr0NztkgL+MwBEEDGUgCNGYDsL1vcfwRGJCfjyeHndq/Xf1OBulxTwnwEggoghBBw5AttZsL7/CI6ggPXgbpcU8J8BIIKIvgWsEIHtLFjffwRHUMB6cLdLCvjPABBBRJ8CVorAdhas7z+CIyhgPbjbJQX8ZwCIIKIvAStGYDsL1vcfwREUsB7c7ZIC/jMARBDRh4CVI7CdBev7j+AIClgP7nZJAf8ZACKI0BZwDw8k21mwvv8IjqCA9eBulxTwnwEggghNARcFKGBRCe31/UdwRHoC3kVLxNztkgL+MwBEEKEl4FUBClhUQnt9/xEcQQFTwLUl1Av4zwAQQYSGgDcKUMCiEtrr+4/giMQEPHl4d5e3la6Ixd0uKeA/A0AEEbEFvFOAAhaV0F7ffwRHJCbgXuFulxTwnwEggoiYAq4sQAGLSmiv7z+CIyhgPbjbJQX8ZwCIICKWgPcWoIBFJbTX9x/BERSwHtztkgL+MwBEEBFDwLUFKGBRCe31/UdwBAWsB3e7pID/DAARRHQVcGMBClhUQnt9/xEcQQHrwd0uKeA/A0AEEV0ELCpAAYtKaK/vP4IjKGA9uNslBfxnAIggoq2AxQUoYFEJ7fX9R3AEBawHd7ukgP8MABFEtBFwUAEKWFRCe33/ERxBAevB3S4p4D8DQAQRoQIOLkABi0por+8/giMoYD242yUF/GcAiCAiSMBtClDAohLa6/uP4AgKWA/udkkB/xkAIoiQC7hlAQpYVEJ7ff8RHEEB68HdLingPwNABBFCAbcvQAGLSmiv7z+CIyhgPbjbJQX8ZwCIIEIi4E4FKGBRCe31/UdwBAWsB3e7pID/DAARRDQJuHMBClhUQnt9/xEcQQHrwd0uKeA/A0AEEXUCjlKAAhaV0F7ffwRHUMB6cLdLCvjPABBBxD4BRytAAYtKaK/vP4IjKGA9uNslBfxnAIggolLAMQtQwKIS2uv7j+AIClgP7nZJAf8ZACKI2BVw5AIUsKiE9vr+IziCAu7Ed4QMTk/9/GiTnsKRxIjXzR6ggPXgf25LCvjPABBBxCO9575z+AxYVEJ7ff8RHEEB68HdLingPwNABBGPVO07pYCFJbTX9x/BERSwHtztkgL+MwBEEPFI1b5TClhYQnt9/xEcQQHrwd0uKeA/A0AEEY9U7TulgIUltNf3H8ERFLAe3O2SAv4zAEQQAdALABHYzlBQwHpwt0sK+M8AEEEEQC8ARGA7Q0EB68HdLingPwNABBEAvQAQge0MBQWsB3e7pID/DAARRAD0AkAEtjMUFLAe3O2SAv4zAEQQAdALABHYzlBQwHpwt0sK+M8AEEEEQC8ARGA7Q0EB68HdLingPwNABBEAvQAQge0MBQWsB3e7pID/DAARRAD0AkAEtjMUFLAe3O2SAv4zAEQQAdALABHYzlBQwHpwt0sK+M8AEEEEQC8ARGA7Q0EB68HdLingPwNABBEAvQAQge0MBQWsB3e7pID/DAARRAD0AkAEtjMUFLAe3O2SAv4zAEQQAdALABHYzlBQwHpwt0sK+M8AEEEEQC8ARGA7Q0EB68HdLingPwNABBEAvQAQge0MBQWsB3e7pID/DAARRAD0AkAEtjMUFLAe3O2SAv4zAEQQAdALABHYzlBQwHpwt0sK+M8AEEEEQC8ARGA7Q0EB68HdLingPwNABBEAvQAQge0MBQWsB3e7pID/DAARRAD0AkAEtjMUFLAe3O2SAv4zAEQQAdALABHYzlBQwHpwt0sK+M8AEEEEQC8ARGA7Q0EB68HdLingPwNABBEAvQAQge0MBQWsB3e7pID/DAARRAD0AkAEtjMUFLAe3O2SAv4zAEQQAdALABHYzlBQwHpwt0sK+M8AEEEEQC8ARGA7Q0EB68HdLingPwNABBEAvQAQge0MBQWsB3e7pID/DAARRAD0AkAEtjMUFLAe3O2SAv4zAEQQAdALABHYzlBQwHpwt0sK+M8AEEEEQC8ARGA7Q0EB68HdLingPwNABBEAvQAQge0MBQWsB3e7pID/DAARRAD0AkAEtjMUFLAe3O2SAv4zAEQQAdALABHYzlBQwHpwt0sK+M8AEEEEQC8ARGA7Q0EB68HdLingPwNABBEAvQAQge0MBQWsB3e7pID/DAARRAD0AkAEtjMUFLAe3O2SAv4zAEQQAdALABHYzlBQwHpwt0sK+M8AEEEEQC8ARGA7Q0EB68HdLingPwNABBEAvQAQge0MBQWsB3e7pID/DAARRAD0AkAEtjMUFLAe3O2SAv4zAEQQAdALABHYzlBQwHpwt0sK+M8AEEEEQC8ARGA7Q0EB68HdLingPwNABBEAvQAQge0MBQWsB3e7pID/DAARRAD0AkAEtjMUFLAe3O2SAv4zAEQQAdALABHYzlBQwHpwt0sK+M8AEEEEQC8ARGA7Q0EB68HdLingPwNABBEAvQAQge0MBQWsB3e7pID/DAARRAD0AkAEtjMUFDAhhBAyABQwIYQQMgAUMCGEEDIAFDAhhBAyABQwIYQQMgAUMCGEEDIAFDAhhBAyABQwIYQQMgAUMCGEEDIAFDAhhBAyABQwIYQQMgAUMCGEEDIAFDAhhBAyABQwIYQQMgAUMCGEEDIAFDAhhBAyABSwV/7zb4+O3vj5F0P/Gp35/sFP/zj079CBl79/cHT0X/9u6F/DPyD9zHYmIVDATvn9UcEb/zz0L9KRlx8ceZ5YP7w7Pw5/PfQv4h2QfmY7kyAoYJ88O3rj72b75YOjn/zb0L9KN54eeZ5Ys3n75hfTl//PvTiGBqWf2c4kCArYJbOd8vf5nz++O//TLd8/cD2xni2E8fToZ0P/Kq5B6We2MwmDAnbJj+8unil8fvQ3A/8qnZgN3v/l+E2zpTdIR0D6me1MAqGAfeN7YM1+/Z95Pmtl5Q0SCd/9zHYmgVDArvnxXdfv1jw7+ukfPU+s/Hf/j/9+dPTmPw39m4Dgu5/ZziQUCtg1vt+sKcat84n16/lpo56ftxnCdT+znUkwFLBnnvk+XbF4vdHzxHqWf2Ljj9OXv/d9HMzgu5/ZziQYCtgxzx684fmkiafFCaPOJ9b8ucLnnp+5mcF3P7OdSTgUsF+e+v4P1e8fFL++54m1iOA7hBl89zPbmbSAAnaL9xeKnh6t8Hry5fcPFr/56i+kNc77me3+zHZ2AAAGt0lEQVRMWkABO+Xl50dv+t4kABPr5QcLZzzzfPkFE7jvZ7YzaQEF7JTPUfaI65e7lm+W+f74qgVQ+pntTEKggH3yFGReOZ9Y3z84+vkXU5422hmYfmY7kxAoYJf8+O7q9S7v5yu6nljTZw/mN/FxfPauBXD6me1MQqCAXfLsCGVgOZ9Y0x9+PZtZ/8P9bWwHBqef2c4kBAqYEEIIGQAKmBBCCBkACpgQQggZAAqYEEIIGQAKmBBCCBkACpgQQggZAAqYEEIIGQAKmBBCCBkACpgQQggZAAqYEEIIGQAKmBBCCBkACpgQQggZAAqYEEIIGQAKmKx58fFboyzLDu99u/Ht0yy79qT0VemLrX9tg1v7fn79T6aXx9n1kN9wb/EtzrJXPgtZlwDCdibWoYDJkufvrafNzdJcuJhNsYNfrb4Mnlg7P1/6qnFiffknT8p/Losvv70PTqzkYTsT+1DAZEE+WNZcXW/4s+zq7dJcCZ5YOz9fegbRNLGWxbb+bHzmwImVOmxn4gAKmMwpBtZr7+eD4Ov3Zn9fDZLJSXa9PAAapkX+r+98Y/PnS9MsdGLJfgdOrORhOxMPUMCkYDZXslc+XX71/Hj9utpslN0/z7L7y38WOrF2fz5bv2rHiUU0YDsTF1DApGA2U8p7fDZmlpPkdPYPZoNlNSJCJ9buz7/yf1ev2nFiEQ3YzsQFFDApOC39R33Ohzffn/9lPlPO1k8hAidWxc+/8tnq1NHdiVW8YJgd3strnC3efTtbvQtXFF99OfvpxYt/qwn1+K3Z85HXn6y+fp4vd3Bz+WSo+DJ77X3pw0J8wnYmLqCASc5s7+/5b+z5y22zpxDL97kCJ1bFzxdPIeYTbHtiTT4snzYTPLHyVx5zXnlvEedsudzrxb/2ZcVJOQQPtjPxAQVMckqv0W1xWuz92ShYzqnAiVXx87NvnC9etdueWLMJ8/rs+89vL06bqT9tdGdinRY/Xoy94uvZcvmzhRcfzkfWLOW12ZeTj7L6VwqJc9jOxAcUMMk5L19OoMxypJytXtMLm1hVP58Pk8VrhFsTa/Xl8l22sIm1emJyli2/vr785/lTlNUread8Tw0atjPxAQVMctYTa/my1+JFteW7XevNHzaxqn5+MUzyP7Ym1vnyvbXZKovnACETazWIFi9Brs9dmf9WZ+UrKBBc2M7EBxQwydk3sdavtZ2u/3M7YGJV/vzy5bTrNaeNnraYWKXSp1tfz3/iPD+B5XeyR4Q4hu1MfEABk5z1f9FvTqyN6wnNp0PQxKr8+dUJJvcrJ9aLrz95+GrWYmKVFlt+XSJfb35OzJWtiwMTNNjOxAcUMMkpf7KxYPHy1ll5z1d9hnE5FVYvhm1OrMqfnz93KF6125lYX75anjDBE2t9vcDKiVV8rCPnKj+5gQzbmfiAAiYF2x+cnE+s1fOH0lQKmVjVP7948S5/1W5rYs3//cO7v/i2zWt2FU8ZKl4Q/PphMRTv7/4TAgPbmbiAAiYFW5cOWkys8tmk51sfpdjDxsSq/vlFoXw8/bfNobL42Mb635JOrO03yZZfV/+q+Qc3Gq49RFzDdiYuoIBJQT4+SudUPj8pvjrdvNXaxhDZv1D5VjFVP78cjfN31CrPcdkutndiXV/+4MZpoouv1+WLlbcnGoGF7UxcQAGTOcWLb1ffz8/n+OY3t+evsG2+lbZ4WS9gYu35+dWwONs7sc5Eb5qt/v3zrQ9Krj84uVhvfgGjs9LHQviUARq2M/EABUwWXNwuv8GVXf10+3oGi8v9lO+TWvHf3eWJtefnVz9VvEdW8Zrd17eXb7HlM2by7frPxahafzu/FtDk49HGpYI2Lh2Un5+yfI0uv2TgO0+KJ0R80wwctjNxAAVMlqwvXJvNN/bkZOOT/pPl63jSibXv59c/dTHanFiXy6l586P5TDlfPKlY/rmYWMsvl2fFXPtosegiwtX/s33x3PkzhPXHSKovlERwYDsT+1DAZM3k8Vv5lr6y+HT/+iWvOWeLC8hLJ9a+ny/91NnWZWwnH786v7rA8v2wL/OZ9mT15/K1u+W3i3//yjvrt8u+3rl9TH6S6Op+MZOPD/OAr/OTkwnAdibWoYAJIYSQAaCACSGEkAGggAkhhJABoIAJIYSQAaCACSGEkAGggAkhhJABoIAJIYSQAaCACSGEkAGggAkhhJABoIAJIYSQAaCACSGEkAGggAkhhJABoIAJIYSQAaCACSGEkAGggAkhhJABoIAJIYSQAaCACSGEkAGggAkhhJABoIAJIYSQAaCACSGEkAH4/81hU6fMp43kAAAAAElFTkSuQmCC" width="960" /></p>
</div>
<div id="figure-c4" class="section level2">
<h2>Figure C4</h2>
<pre class="r"><code>pred_scl &lt;- avg_predictions(model_soccultl3, by = &quot;migrationb&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as losing groups&quot;,
         outcome = &quot;overall\nsociocultural&quot;)
pred_scw &lt;- avg_predictions(model_soccultw3, by = &quot;migrationb&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as winning groups&quot;,
         outcome = &quot;overall\nsociocultural&quot;)


# race
pred_scrl &lt;- avg_predictions(model_racel3, by = &quot;migrationb&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as losing groups&quot;,
         outcome = &quot;race&quot;)
pred_scrw &lt;- avg_predictions(model_racew3, by = &quot;migrationb&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as winning groups&quot;,
         outcome = &quot;race&quot;)

# gender

pred_scgl &lt;- avg_predictions(model_gsl3, by = &quot;migrationb&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as losing groups&quot;,
         outcome = &quot;gender and\nsexuality&quot;)
pred_scgw &lt;- avg_predictions(model_gsw3, by = &quot;migrationb&quot;) %&gt;% 
  mutate(question = &quot;Mentioned as winning groups&quot;,
         outcome = &quot;gender and\nsexuality&quot;)

# combine

pred_cult &lt;- bind_rows(pred_scl, pred_scw, pred_scrl, pred_scrw, pred_scgl, pred_scgw) %&gt;% 
  mutate(outcome = fct_relevel(factor(outcome), 
                               &quot;overall\nsociocultural&quot;, &quot;race&quot;, &quot;gender and\nsexuality&quot;),
         question = fct_rev(factor(question)))

ggplot(pred_cult, aes(outcome, estimate, color = migrationb)) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high),
                  position = position_dodge(width = 0.5)) +
  facet_wrap(~question) +
  scale_color_grey(start = 0, end = 0.7, name = &quot;Migration background&quot;)+
  theme(legend.position = &quot;bottom&quot;)+
  labs(y = &quot;Predicted probability of mentioning groups&quot;, x = NULL
       )</code></pre>
<p><img role="img" 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" width="768" /></p>
</div>
<div id="figure-c5" class="section level2">
<h2>Figure C5</h2>
<pre class="r"><code>model_soccultl7 &lt;- glm(sociocultural ~ galtan_group + att_socialben + age_group * gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_soccultw7 &lt;- glm(sociocultural ~ galtan_group + att_socialben + age_group * gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)

model_racel7 &lt;- glm(soccult_race ~ galtan_group + att_socialben + age_group * gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_racew7 &lt;- glm(soccult_race ~ galtan_group + att_socialben + age_group * gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)

model_gsl7 &lt;- glm(soccult_gendersex ~ galtan_group + att_socialben + age_group * gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_gsw7 &lt;- glm(soccult_gendersex ~ galtan_group + att_socialben + age_group * gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)


a1 &lt;- plot_predictions(model_soccultw7, by = c(&quot;age_group&quot;, &quot;gender_fm&quot;)) + 
  labs(title = &quot;Winning groups&quot;, subtitle = &quot;Sociocultural overall&quot;,
       x = NULL, y = NULL) +
  theme(legend.position = &quot;none&quot;) +
  scale_color_grey(start = 0, end = 0.7)+
  scale_y_continuous(limits = c(0, 0.24))

a2 &lt;- plot_predictions(model_soccultl7, by = c(&quot;age_group&quot;, &quot;gender_fm&quot;)) + 
  labs(title = &quot;Losing groups&quot;,  subtitle = &quot;Sociocultural overall&quot;,
       x = NULL, y = NULL) +
  theme(legend.position = &quot;none&quot;)+
  scale_color_grey(start = 0, end = 0.7)+
  scale_y_continuous(limits = c(0, 0.24))

# Race

a3 &lt;- plot_predictions(model_racew7, by = c(&quot;age_group&quot;, &quot;gender_fm&quot;)) + 
  labs(subtitle = &quot;Race&quot;,
       x = NULL, y = &quot;Predicted probability of mentioning&quot;) +
  theme(legend.position = &quot;none&quot;)+
  scale_color_grey(start = 0, end = 0.7)+
  scale_y_continuous(limits = c(0, 0.24))

a4 &lt;- plot_predictions(model_racel7, by = c(&quot;age_group&quot;, &quot;gender_fm&quot;)) + 
  labs(subtitle = &quot;Race&quot;,
       x = NULL, y = NULL) +
  theme(legend.position = &quot;none&quot;)+
  scale_color_grey(start = 0, end = 0.7)+
  scale_y_continuous(limits = c(0, 0.24))

# Gender and sexuality

a5 &lt;- plot_predictions(model_gsw7, by = c(&quot;age_group&quot;, &quot;gender_fm&quot;)) + 
  labs(subtitle = &quot;Gender/sexuality&quot;, color = NULL,
       x = &quot;Age&quot;, y = NULL) +
  theme(legend.position = &quot;bottom&quot;)+
  scale_color_grey(start = 0, end = 0.7)+
  scale_y_continuous(limits = c(0, 0.24))

a6 &lt;- plot_predictions(model_gsl7, by = c(&quot;age_group&quot;, &quot;gender_fm&quot;)) + 
  labs(subtitle = &quot;Gender/sexuality&quot;, color = NULL,
       x = &quot;Age&quot;, y = NULL) +
  theme(legend.position = &quot;bottom&quot;)+
  scale_color_grey(start = 0, end = 0.7)+
  scale_y_continuous(limits = c(0, 0.24))</code></pre>
<pre class="r"><code>plotsag &lt;- arrangeGrob(a1, a2, a3, a4, a5, a6,
                       ncol = 2, widths = unit(c(9, 9), c(&quot;cm&quot;)),
                       heights = unit(c(8, 7, 9), c(&quot;cm&quot;)))

grid.arrange(plotsag)</code></pre>
<p><img role="img" 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" width="960" /></p>
</div>
<div id="figure-c6" class="section level2">
<h2>Figure C6</h2>
<pre class="r"><code>tstat_key_win_gage &lt;- textstat_keyness(dfm_group(dfmat_win, groups = dfmat_win$gender_age_contrast),
                                  target = &quot;young women&quot;) %&gt;% 
  left_join(dict_df, by = c(&quot;feature&quot;)) %&gt;% 
  mutate(
    targetgroup = if_else(chi2 &gt;= 0, &quot;target&quot;, &quot;reference&quot;),
    chi_abs = abs(chi2) 
  ) %&gt;% 
  group_by(targetgroup) %&gt;% 
  slice_max(chi_abs, n = 20, with_ties = F) %&gt;% 
  filter(!is.nan(chi2))
  
# plot

translations_wingage &lt;- c(&quot;athletes&quot;, &quot;demeanor&quot;, &quot;middle class&quot;, &quot;craftsmen&quot;, &quot;happier&quot;, &quot;IT employees&quot;, &quot;nursery school teachers&quot;, &quot;feminists&quot;, &quot;higher&quot;, &quot;social&quot;, &quot;computer scientists&quot;, &quot;caregivers&quot;, &quot;to perform&quot;, &quot;thinkers&quot;, &quot;digitalization&quot;, &quot;brought&quot;, &quot;belong&quot;, &quot;overall&quot;, &quot;IT specialists&quot;, &quot;artists&quot;, &quot;yet&quot;, &quot;achieved&quot;, &quot;societal&quot;, &quot;social media&quot;, &quot;active&quot;, &quot;simple&quot;, &quot;focus&quot;, &quot;to fight&quot;, &quot;white people&quot;, &quot;caregivers&quot;, &quot;family&quot;, &quot;job&quot;, &quot;tolerant&quot;, &quot;good&quot;, &quot;a lot of money&quot;, &quot;to work&quot;, &quot;general&quot;, &quot;mothers&quot;, &quot;men&quot;, &quot;women&quot;)

ggplot(data = tstat_key_win_gage, aes(x = reorder(feature, chi2), y = chi2, fill = dimension,
                                     label = feature
                                     )) + 
  geom_col(width = 0.7, linewidth = 0.2, color = &quot;black&quot;) +
  geom_text(color = &quot;gray25&quot;, hjust = &quot;outward&quot;, 
            position = position_nudge_center(y = 0.4,
                                               direction = &quot;split&quot;)
    ) +
  coord_flip() +
  scale_x_discrete(labels = translations_wingage) +
  scale_y_continuous(
    limits = c(-10, 10)
    ) +
  scale_fill_manual(values = c(&quot;gray1&quot;, 
                                      #&quot;gray40&quot;, 
                                      &quot;gray72&quot;),
                    na.value = &quot;gray99&quot;,
                    name = NULL, na.translate = F) +
  labs(x = NULL, y = &quot;Chi2&quot;) +
  theme(legend.position = &quot;bottom&quot;,
        axis.text.y = element_text(size = 8)) +
  annotate(geom = &quot;text&quot;, x = c(-1, 42), y = c(-5, 5), size = 4, color = &quot;black&quot;,
           label = c(&quot;young men&quot;, &quot;young women&quot;))+
  expand_limits(x = c(-2, 43))</code></pre>
<p><img role="img" 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width="960" /></p>
</div>
<div id="table-c3" class="section level2">
<h2>Table C3</h2>
<pre class="r"><code>cm &lt;- c(
  &quot;poc_win&quot; = &quot;Mentioned migrants as winners&quot;,
  &quot;männer_lose&quot; = &quot;Mentioned men as losers&quot;,
  &quot;frauen_win&quot; = &quot;Mentioned women as winners&quot;,
  &quot;galtan_groupsocial conservative&quot; = &quot;Social conservative group&quot;,
  &quot;econlr_groupecon. right&quot; = &quot;Econ. right group&quot;,
  &quot;galtan_att&quot; = &quot;TAN attitudes (cont.)&quot;,
  &quot;I(galtan_att^2)&quot; = &quot;TAN attitudes^2&quot;,
  &quot;poc_win:galtan_att&quot; = &quot;Migrant winners x TAN attitudes&quot;,
  &quot;att_socialben&quot; = &quot;Anti-redistribution attitude (cont.)&quot;,
  &quot;age&quot; = &quot;Age&quot;,
  &quot;gender_fmwomen&quot; = &quot;Woman&quot;,
  &quot;edumedium education&quot; = &quot;Medium education (ref. low)&quot;,
  &quot;eduhigh education&quot; = &quot;High education (ref. low)&quot;,
  &quot;inc_hh&quot; = &quot;Household-equivalized income&quot;,
  &quot;migrationbyes&quot; = &quot;With migration background&quot;,
  &quot;urbansuburb/small city&quot; = &quot;Suburbs/small city (ref. country)&quot;,
  &quot;urbanbig city&quot; = &quot;Big city (ref. country)&quot;
)

models &lt;- list(
  &quot;Winning&quot; = model_soccultw3,
  &quot;Winning 2&quot; = model_soccultw2,
  &quot;Winning 3&quot; = model_soccultw2b,
  &quot;Losing&quot; = model_soccultl3,
  &quot;Losing 2&quot; = model_soccultl2,
  &quot;Losing 3&quot; = model_soccultl2b
  )

modelsummary(models, coef_map = cm, 
             stars = T,
             #output = &quot;latex&quot;,
             title = &quot;Predictors of mentioning sociocultural dictionary dimension&quot;,
             notes = &quot;Binomial generalized linear model. Standard errors in parentheses, conf. level: 0.95.&quot;)</code></pre>
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        <thead>
        <caption>Predictors of mentioning sociocultural dictionary dimension</caption>
              <tr>
                <th scope="col"> </th>
                <th scope="col">Winning</th>
                <th scope="col">Winning 2</th>
                <th scope="col">Winning 3</th>
                <th scope="col">Losing</th>
                <th scope="col">Losing 2</th>
                <th scope="col">Losing 3</th>
              </tr>
        </thead>
        <tfoot><tr><td colspan="7">+ p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td></tr>
<tr><td colspan="7">Binomial generalized linear model. Standard errors in parentheses, conf. level: 0.95.</td></tr></tfoot>
        <tbody>
                <tr>
                  <td>Social conservative group</td>
                  <td>-0.170</td>
                  <td></td>
                  <td></td>
                  <td>0.226</td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.115)</td>
                  <td></td>
                  <td></td>
                  <td>(0.142)</td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>TAN attitudes (cont.)</td>
                  <td></td>
                  <td>-0.067+</td>
                  <td>-0.343*</td>
                  <td></td>
                  <td>0.119**</td>
                  <td>0.049</td>
                </tr>
                <tr>
                  <td></td>
                  <td></td>
                  <td>(0.038)</td>
                  <td>(0.143)</td>
                  <td></td>
                  <td>(0.046)</td>
                  <td>(0.187)</td>
                </tr>
                <tr>
                  <td>TAN attitudes^2</td>
                  <td></td>
                  <td></td>
                  <td>0.037*</td>
                  <td></td>
                  <td></td>
                  <td>0.009</td>
                </tr>
                <tr>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td>(0.019)</td>
                  <td></td>
                  <td></td>
                  <td>(0.023)</td>
                </tr>
                <tr>
                  <td>Anti-redistribution attitude (cont.)</td>
                  <td>-0.048</td>
                  <td>-0.038</td>
                  <td>-0.029</td>
                  <td>0.007</td>
                  <td>-0.017</td>
                  <td>-0.015</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.031)</td>
                  <td>(0.033)</td>
                  <td>(0.033)</td>
                  <td>(0.038)</td>
                  <td>(0.040)</td>
                  <td>(0.040)</td>
                </tr>
                <tr>
                  <td>Age</td>
                  <td>-0.012***</td>
                  <td>-0.011***</td>
                  <td>-0.011**</td>
                  <td>0.000</td>
                  <td>-0.001</td>
                  <td>-0.001</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.003)</td>
                  <td>(0.003)</td>
                  <td>(0.003)</td>
                  <td>(0.004)</td>
                  <td>(0.004)</td>
                  <td>(0.004)</td>
                </tr>
                <tr>
                  <td>Woman</td>
                  <td>0.411***</td>
                  <td>0.407***</td>
                  <td>0.401***</td>
                  <td>0.275*</td>
                  <td>0.280*</td>
                  <td>0.278*</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.103)</td>
                  <td>(0.103)</td>
                  <td>(0.103)</td>
                  <td>(0.128)</td>
                  <td>(0.128)</td>
                  <td>(0.128)</td>
                </tr>
                <tr>
                  <td>Medium education (ref. low)</td>
                  <td>-0.050</td>
                  <td>-0.055</td>
                  <td>-0.052</td>
                  <td>0.035</td>
                  <td>0.049</td>
                  <td>0.050</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.137)</td>
                  <td>(0.137)</td>
                  <td>(0.137)</td>
                  <td>(0.167)</td>
                  <td>(0.167)</td>
                  <td>(0.167)</td>
                </tr>
                <tr>
                  <td>High education (ref. low)</td>
                  <td>0.111</td>
                  <td>0.099</td>
                  <td>0.097</td>
                  <td>-0.034</td>
                  <td>0.001</td>
                  <td>-0.000</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.157)</td>
                  <td>(0.157)</td>
                  <td>(0.157)</td>
                  <td>(0.199)</td>
                  <td>(0.199)</td>
                  <td>(0.199)</td>
                </tr>
                <tr>
                  <td>Household-equivalized income</td>
                  <td>0.000</td>
                  <td>0.000</td>
                  <td>0.000</td>
                  <td>0.000</td>
                  <td>0.000</td>
                  <td>0.000</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                </tr>
                <tr>
                  <td>With migration background</td>
                  <td>0.019</td>
                  <td>0.021</td>
                  <td>0.025</td>
                  <td>0.115</td>
                  <td>0.110</td>
                  <td>0.111</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.139)</td>
                  <td>(0.139)</td>
                  <td>(0.140)</td>
                  <td>(0.172)</td>
                  <td>(0.173)</td>
                  <td>(0.173)</td>
                </tr>
                <tr>
                  <td>Suburbs/small city (ref. country)</td>
                  <td>0.019</td>
                  <td>0.016</td>
                  <td>0.017</td>
                  <td>0.125</td>
                  <td>0.131</td>
                  <td>0.132</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.128)</td>
                  <td>(0.128)</td>
                  <td>(0.128)</td>
                  <td>(0.164)</td>
                  <td>(0.164)</td>
                  <td>(0.164)</td>
                </tr>
                <tr>
                  <td>Big city (ref. country)</td>
                  <td>0.129</td>
                  <td>0.130</td>
                  <td>0.129</td>
                  <td>0.391*</td>
                  <td>0.393*</td>
                  <td>0.393*</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.142)</td>
                  <td>(0.142)</td>
                  <td>(0.142)</td>
                  <td>(0.179)</td>
                  <td>(0.179)</td>
                  <td>(0.179)</td>
                </tr>
                <tr>
                  <td>Num.Obs.</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                </tr>
                <tr>
                  <td>AIC</td>
                  <td>2711.1</td>
                  <td>2710.1</td>
                  <td>2708.2</td>
                  <td>1953.3</td>
                  <td>1949.3</td>
                  <td>1951.1</td>
                </tr>
                <tr>
                  <td>BIC</td>
                  <td>2778.2</td>
                  <td>2777.2</td>
                  <td>2781.4</td>
                  <td>2020.4</td>
                  <td>2016.4</td>
                  <td>2024.3</td>
                </tr>
                <tr>
                  <td>Log.Lik.</td>
                  <td>-1344.534</td>
                  <td>-1344.047</td>
                  <td>-1342.098</td>
                  <td>-965.664</td>
                  <td>-963.635</td>
                  <td>-963.561</td>
                </tr>
                <tr>
                  <td>F</td>
                  <td>4.782</td>
                  <td>4.892</td>
                  <td>4.857</td>
                  <td>1.425</td>
                  <td>1.826</td>
                  <td>1.685</td>
                </tr>
                <tr>
                  <td>RMSE</td>
                  <td>0.35</td>
                  <td>0.35</td>
                  <td>0.35</td>
                  <td>0.28</td>
                  <td>0.28</td>
                  <td>0.28</td>
                </tr>
        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
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<div id="table-c4" class="section level2">
<h2>Table C4</h2>
<pre class="r"><code>model_racel2 &lt;- glm(soccult_race ~ galtan_att + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_racel2b &lt;- glm(soccult_race ~ galtan_att + I(galtan_att^2) + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_racew2 &lt;- glm(soccult_race ~ galtan_att + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)

model_racew2b &lt;- glm(soccult_race ~ galtan_att + I(galtan_att^2) + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)


models &lt;- list(
  &quot;Winning&quot; = model_racew3,
  &quot;Winning 2&quot; = model_racew2,
  &quot;Winning 3&quot; = model_racew2b,
  &quot;Losing&quot; = model_racel3,
  &quot;Losing 1&quot; = model_racel2,
  &quot;Losing 3&quot; = model_racel2b
  )


modelsummary(models, coef_map = cm, stars = T,
             #output = &quot;latex&quot;,
             title = &quot;Predictors of mentioning racial dictionary dimension&quot;,
             notes = &quot;Binomial generalized linear model. Standard errors in parentheses, conf. level: 0.95.&quot;)</code></pre>
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        <thead>
        <caption>Predictors of mentioning racial dictionary dimension</caption>
              <tr>
                <th scope="col"> </th>
                <th scope="col">Winning</th>
                <th scope="col">Winning 2</th>
                <th scope="col">Winning 3</th>
                <th scope="col">Losing</th>
                <th scope="col">Losing 1</th>
                <th scope="col">Losing 3</th>
              </tr>
        </thead>
        <tfoot><tr><td colspan="7">+ p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td></tr>
<tr><td colspan="7">Binomial generalized linear model. Standard errors in parentheses, conf. level: 0.95.</td></tr></tfoot>
        <tbody>
                <tr>
                  <td>Social conservative group</td>
                  <td>0.074</td>
                  <td></td>
                  <td></td>
                  <td>0.241</td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.183)</td>
                  <td></td>
                  <td></td>
                  <td>(0.169)</td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>TAN attitudes (cont.)</td>
                  <td></td>
                  <td>0.006</td>
                  <td>-0.290</td>
                  <td></td>
                  <td>0.126*</td>
                  <td>0.034</td>
                </tr>
                <tr>
                  <td></td>
                  <td></td>
                  <td>(0.060)</td>
                  <td>(0.229)</td>
                  <td></td>
                  <td>(0.055)</td>
                  <td>(0.223)</td>
                </tr>
                <tr>
                  <td>TAN attitudes^2</td>
                  <td></td>
                  <td></td>
                  <td>0.039</td>
                  <td></td>
                  <td></td>
                  <td>0.012</td>
                </tr>
                <tr>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td>(0.029)</td>
                  <td></td>
                  <td></td>
                  <td>(0.028)</td>
                </tr>
                <tr>
                  <td>Anti-redistribution attitude (cont.)</td>
                  <td>-0.005</td>
                  <td>0.001</td>
                  <td>0.011</td>
                  <td>0.024</td>
                  <td>-0.002</td>
                  <td>0.001</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.050)</td>
                  <td>(0.052)</td>
                  <td>(0.052)</td>
                  <td>(0.046)</td>
                  <td>(0.047)</td>
                  <td>(0.047)</td>
                </tr>
                <tr>
                  <td>Age</td>
                  <td>-0.010+</td>
                  <td>-0.010+</td>
                  <td>-0.010+</td>
                  <td>0.001</td>
                  <td>0.001</td>
                  <td>0.001</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.005)</td>
                  <td>(0.005)</td>
                  <td>(0.005)</td>
                  <td>(0.005)</td>
                  <td>(0.005)</td>
                  <td>(0.005)</td>
                </tr>
                <tr>
                  <td>Woman</td>
                  <td>0.059</td>
                  <td>0.059</td>
                  <td>0.051</td>
                  <td>0.108</td>
                  <td>0.114</td>
                  <td>0.112</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.163)</td>
                  <td>(0.163)</td>
                  <td>(0.163)</td>
                  <td>(0.151)</td>
                  <td>(0.151)</td>
                  <td>(0.152)</td>
                </tr>
                <tr>
                  <td>Medium education (ref. low)</td>
                  <td>0.061</td>
                  <td>0.056</td>
                  <td>0.058</td>
                  <td>-0.044</td>
                  <td>-0.030</td>
                  <td>-0.029</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.219)</td>
                  <td>(0.219)</td>
                  <td>(0.219)</td>
                  <td>(0.195)</td>
                  <td>(0.195)</td>
                  <td>(0.195)</td>
                </tr>
                <tr>
                  <td>High education (ref. low)</td>
                  <td>0.259</td>
                  <td>0.247</td>
                  <td>0.242</td>
                  <td>-0.071</td>
                  <td>-0.035</td>
                  <td>-0.037</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.250)</td>
                  <td>(0.251)</td>
                  <td>(0.251)</td>
                  <td>(0.233)</td>
                  <td>(0.233)</td>
                  <td>(0.233)</td>
                </tr>
                <tr>
                  <td>Household-equivalized income</td>
                  <td>-0.000</td>
                  <td>-0.000</td>
                  <td>-0.000</td>
                  <td>0.000</td>
                  <td>0.000</td>
                  <td>0.000</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                </tr>
                <tr>
                  <td>With migration background</td>
                  <td>-0.348</td>
                  <td>-0.347</td>
                  <td>-0.343</td>
                  <td>0.025</td>
                  <td>0.020</td>
                  <td>0.021</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.247)</td>
                  <td>(0.247)</td>
                  <td>(0.247)</td>
                  <td>(0.211)</td>
                  <td>(0.211)</td>
                  <td>(0.211)</td>
                </tr>
                <tr>
                  <td>Suburbs/small city (ref. country)</td>
                  <td>0.307</td>
                  <td>0.306</td>
                  <td>0.306</td>
                  <td>-0.043</td>
                  <td>-0.036</td>
                  <td>-0.036</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.222)</td>
                  <td>(0.222)</td>
                  <td>(0.222)</td>
                  <td>(0.192)</td>
                  <td>(0.192)</td>
                  <td>(0.192)</td>
                </tr>
                <tr>
                  <td>Big city (ref. country)</td>
                  <td>0.532*</td>
                  <td>0.531*</td>
                  <td>0.531*</td>
                  <td>0.335</td>
                  <td>0.337</td>
                  <td>0.337</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.238)</td>
                  <td>(0.238)</td>
                  <td>(0.238)</td>
                  <td>(0.207)</td>
                  <td>(0.207)</td>
                  <td>(0.207)</td>
                </tr>
                <tr>
                  <td>Num.Obs.</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                </tr>
                <tr>
                  <td>AIC</td>
                  <td>1330.5</td>
                  <td>1330.7</td>
                  <td>1331.0</td>
                  <td>1499.8</td>
                  <td>1496.7</td>
                  <td>1498.5</td>
                </tr>
                <tr>
                  <td>BIC</td>
                  <td>1397.7</td>
                  <td>1397.8</td>
                  <td>1404.2</td>
                  <td>1567.0</td>
                  <td>1563.8</td>
                  <td>1571.7</td>
                </tr>
                <tr>
                  <td>Log.Lik.</td>
                  <td>-654.269</td>
                  <td>-654.346</td>
                  <td>-653.477</td>
                  <td>-738.922</td>
                  <td>-737.349</td>
                  <td>-737.260</td>
                </tr>
                <tr>
                  <td>F</td>
                  <td>1.335</td>
                  <td>1.322</td>
                  <td>1.362</td>
                  <td>0.935</td>
                  <td>1.245</td>
                  <td>1.163</td>
                </tr>
                <tr>
                  <td>RMSE</td>
                  <td>0.22</td>
                  <td>0.22</td>
                  <td>0.22</td>
                  <td>0.24</td>
                  <td>0.24</td>
                  <td>0.24</td>
                </tr>
        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
<div id="table-c5" class="section level2">
<h2>Table C5</h2>
<pre class="r"><code>model_gsl2 &lt;- glm(soccult_gendersex ~ galtan_att + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_gsl2b &lt;- glm(soccult_gendersex ~ galtan_att + I(galtan_att^2) + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_gsw2 &lt;- glm(soccult_gendersex ~ galtan_att + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)

model_gsw2b &lt;- glm(soccult_gendersex ~ galtan_att + I(galtan_att^2) + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)

models &lt;- list(
  &quot;Winning&quot; = model_gsw3,
  &quot;Winning 2&quot; = model_gsw2,
  &quot;Winning 3&quot; = model_gsw2b,
  &quot;Losing&quot; = model_gsl3,
  &quot;Losing 2&quot; = model_gsl2,
  &quot;Losing 3&quot; = model_gsl2b
  )

modelsummary(models, coef_map = cm, stars = T,
             #output = &quot;latex&quot;,
             title = &quot;Predictors of mentioning gender/sexuality dictionary dimensions&quot;,
             notes = &quot;Binomial generalized linear model. Standard errors in parentheses, conf. level: 0.95.&quot;)</code></pre>
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        <thead>
        <caption>Predictors of mentioning gender/sexuality dictionary dimensions</caption>
              <tr>
                <th scope="col"> </th>
                <th scope="col">Winning</th>
                <th scope="col">Winning 2</th>
                <th scope="col">Winning 3</th>
                <th scope="col">Losing</th>
                <th scope="col">Losing 2</th>
                <th scope="col">Losing 3</th>
              </tr>
        </thead>
        <tfoot><tr><td colspan="7">+ p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td></tr>
<tr><td colspan="7">Binomial generalized linear model. Standard errors in parentheses, conf. level: 0.95.</td></tr></tfoot>
        <tbody>
                <tr>
                  <td>Social conservative group</td>
                  <td>-0.217+</td>
                  <td></td>
                  <td></td>
                  <td>0.301</td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.132)</td>
                  <td></td>
                  <td></td>
                  <td>(0.231)</td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>TAN attitudes (cont.)</td>
                  <td></td>
                  <td>-0.081+</td>
                  <td>-0.277+</td>
                  <td></td>
                  <td>0.162*</td>
                  <td>0.067</td>
                </tr>
                <tr>
                  <td></td>
                  <td></td>
                  <td>(0.043)</td>
                  <td>(0.164)</td>
                  <td></td>
                  <td>(0.073)</td>
                  <td>(0.297)</td>
                </tr>
                <tr>
                  <td>TAN attitudes^2</td>
                  <td></td>
                  <td></td>
                  <td>0.027</td>
                  <td></td>
                  <td></td>
                  <td>0.012</td>
                </tr>
                <tr>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td>(0.022)</td>
                  <td></td>
                  <td></td>
                  <td>(0.037)</td>
                </tr>
                <tr>
                  <td>Anti-redistribution attitude (cont.)</td>
                  <td>-0.082*</td>
                  <td>-0.072+</td>
                  <td>-0.065+</td>
                  <td>-0.089</td>
                  <td>-0.123+</td>
                  <td>-0.120+</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.036)</td>
                  <td>(0.038)</td>
                  <td>(0.038)</td>
                  <td>(0.063)</td>
                  <td>(0.064)</td>
                  <td>(0.065)</td>
                </tr>
                <tr>
                  <td>Age</td>
                  <td>-0.015***</td>
                  <td>-0.014***</td>
                  <td>-0.014***</td>
                  <td>-0.007</td>
                  <td>-0.008</td>
                  <td>-0.008</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.004)</td>
                  <td>(0.004)</td>
                  <td>(0.004)</td>
                  <td>(0.007)</td>
                  <td>(0.007)</td>
                  <td>(0.007)</td>
                </tr>
                <tr>
                  <td>Woman</td>
                  <td>0.602***</td>
                  <td>0.597***</td>
                  <td>0.593***</td>
                  <td>0.620**</td>
                  <td>0.628**</td>
                  <td>0.625**</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.120)</td>
                  <td>(0.120)</td>
                  <td>(0.120)</td>
                  <td>(0.214)</td>
                  <td>(0.214)</td>
                  <td>(0.214)</td>
                </tr>
                <tr>
                  <td>Medium education (ref. low)</td>
                  <td>-0.059</td>
                  <td>-0.065</td>
                  <td>-0.062</td>
                  <td>0.237</td>
                  <td>0.259</td>
                  <td>0.260</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.157)</td>
                  <td>(0.157)</td>
                  <td>(0.157)</td>
                  <td>(0.291)</td>
                  <td>(0.291)</td>
                  <td>(0.291)</td>
                </tr>
                <tr>
                  <td>High education (ref. low)</td>
                  <td>0.016</td>
                  <td>0.003</td>
                  <td>0.002</td>
                  <td>0.239</td>
                  <td>0.296</td>
                  <td>0.294</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.181)</td>
                  <td>(0.181)</td>
                  <td>(0.181)</td>
                  <td>(0.335)</td>
                  <td>(0.336)</td>
                  <td>(0.336)</td>
                </tr>
                <tr>
                  <td>Household-equivalized income</td>
                  <td>0.000*</td>
                  <td>0.000*</td>
                  <td>0.000*</td>
                  <td>0.000</td>
                  <td>0.000</td>
                  <td>0.000</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                </tr>
                <tr>
                  <td>With migration background</td>
                  <td>0.095</td>
                  <td>0.096</td>
                  <td>0.099</td>
                  <td>0.064</td>
                  <td>0.059</td>
                  <td>0.061</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.155)</td>
                  <td>(0.155)</td>
                  <td>(0.155)</td>
                  <td>(0.275)</td>
                  <td>(0.275)</td>
                  <td>(0.275)</td>
                </tr>
                <tr>
                  <td>Suburbs/small city (ref. country)</td>
                  <td>-0.040</td>
                  <td>-0.042</td>
                  <td>-0.041</td>
                  <td>0.552+</td>
                  <td>0.561+</td>
                  <td>0.562+</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.143)</td>
                  <td>(0.143)</td>
                  <td>(0.143)</td>
                  <td>(0.289)</td>
                  <td>(0.289)</td>
                  <td>(0.289)</td>
                </tr>
                <tr>
                  <td>Big city (ref. country)</td>
                  <td>-0.050</td>
                  <td>-0.049</td>
                  <td>-0.049</td>
                  <td>0.564+</td>
                  <td>0.567+</td>
                  <td>0.567+</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.162)</td>
                  <td>(0.162)</td>
                  <td>(0.162)</td>
                  <td>(0.317)</td>
                  <td>(0.317)</td>
                  <td>(0.317)</td>
                </tr>
                <tr>
                  <td>Num.Obs.</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                </tr>
                <tr>
                  <td>AIC</td>
                  <td>2207.6</td>
                  <td>2206.8</td>
                  <td>2207.3</td>
                  <td>920.5</td>
                  <td>917.4</td>
                  <td>919.3</td>
                </tr>
                <tr>
                  <td>BIC</td>
                  <td>2274.7</td>
                  <td>2273.9</td>
                  <td>2280.5</td>
                  <td>987.7</td>
                  <td>984.6</td>
                  <td>992.6</td>
                </tr>
                <tr>
                  <td>Log.Lik.</td>
                  <td>-1092.786</td>
                  <td>-1092.398</td>
                  <td>-1091.638</td>
                  <td>-449.268</td>
                  <td>-447.720</td>
                  <td>-447.667</td>
                </tr>
                <tr>
                  <td>F</td>
                  <td>6.578</td>
                  <td>6.680</td>
                  <td>6.275</td>
                  <td>1.754</td>
                  <td>2.064</td>
                  <td>1.889</td>
                </tr>
                <tr>
                  <td>RMSE</td>
                  <td>0.31</td>
                  <td>0.31</td>
                  <td>0.31</td>
                  <td>0.17</td>
                  <td>0.17</td>
                  <td>0.17</td>
                </tr>
        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
<div id="table-c6" class="section level2">
<h2>Table C6</h2>
<pre class="r"><code>cm &lt;- c(
  &quot;gender_eduwomen without higher ed.&quot; = &quot;Women without higher ed.&quot;,
  &quot;gender_edumen with higher ed.&quot; = &quot;Men with higher ed.&quot;,
  &quot;gender_edumen without higher ed.&quot; = &quot;Men without higher ed.&quot;,
  &quot;galtan_groupsocial conservative&quot; = &quot;Social conservative group&quot;,
  &quot;econlr_groupecon. right&quot; = &quot;Econ. right group&quot;,
  &quot;galtan_att&quot; = &quot;TAN attitudes (cont.)&quot;,
  &quot;I(galtan_att^2)&quot; = &quot;TAN attitudes^2&quot;,
  &quot;poc_win:galtan_att&quot; = &quot;Migrant winners x TAN attitudes&quot;,
  &quot;att_socialben&quot; = &quot;Anti-redistribution attitude (cont.)&quot;,
  &quot;age&quot; = &quot;Age&quot;,
  &quot;inc_hh&quot; = &quot;Household-equivalized income&quot;,
  &quot;migrationbyes&quot; = &quot;With migration background&quot;,
  &quot;urbansuburb/small city&quot; = &quot;Suburbs/small city (ref. country)&quot;,
  &quot;urbanbig city&quot; = &quot;Big city (ref. country)&quot;
)

models &lt;- list(
  &quot;Winning&quot; = list(
    &quot;soc-cult&quot; = model_soccultw4,
    &quot;race&quot; = model_racew4,
    &quot;gender&quot; = model_gsw4
  ),
  &quot;Losing&quot; = list(
    &quot;soc-cult&quot; = model_soccultl4,
    &quot;race&quot; = model_racel4,
    &quot;gender&quot; = model_gsl4
  )
)

modelsummary(models, coef_map = cm, stars = T,
             shape = &quot;cbind&quot;,
             #output = &quot;latex&quot;,
             title = &quot;Predictors of mentioning sociocultural dictionary dimensions, by gender and education&quot;,
             notes = &quot;Binomial generalized linear model (reference category:  women with higher education). Standard errors in parentheses, conf. level: 0.95.&quot;)</code></pre>
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        <thead>
<tr>
<th scope="col" align="center" colspan="1"> </th>
<th scope="col" align="center" colspan="3">Winning</th>
<th scope="col" align="center" colspan="3">Losing</th>
</tr>
        <caption>Predictors of mentioning sociocultural dictionary dimensions, by gender and education</caption>
              <tr>
                <th scope="col"> </th>
                <th scope="col">soc-cult</th>
                <th scope="col">race</th>
                <th scope="col">gender</th>
                <th scope="col">soc-cult </th>
                <th scope="col">race </th>
                <th scope="col">gender </th>
              </tr>
        </thead>
        <tfoot><tr><td colspan="7">+ p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td></tr>
<tr><td colspan="7">Binomial generalized linear model (reference category:  women with higher education). Standard errors in parentheses, conf. level: 0.95.</td></tr></tfoot>
        <tbody>
                <tr>
                  <td>Women without higher ed.</td>
                  <td>-0.168</td>
                  <td>-0.424+</td>
                  <td>-0.076</td>
                  <td>0.010</td>
                  <td>-0.161</td>
                  <td>-0.004</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.157)</td>
                  <td>(0.254)</td>
                  <td>(0.173)</td>
                  <td>(0.212)</td>
                  <td>(0.252)</td>
                  <td>(0.315)</td>
                </tr>
                <tr>
                  <td>Men with higher ed.</td>
                  <td>-0.441*</td>
                  <td>-0.366</td>
                  <td>-0.629**</td>
                  <td>-0.323</td>
                  <td>-0.355</td>
                  <td>-0.499</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.185)</td>
                  <td>(0.294)</td>
                  <td>(0.214)</td>
                  <td>(0.249)</td>
                  <td>(0.293)</td>
                  <td>(0.384)</td>
                </tr>
                <tr>
                  <td>Men without higher ed.</td>
                  <td>-0.554***</td>
                  <td>-0.359</td>
                  <td>-0.652***</td>
                  <td>-0.260</td>
                  <td>-0.189</td>
                  <td>-0.713*</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.167)</td>
                  <td>(0.257)</td>
                  <td>(0.189)</td>
                  <td>(0.220)</td>
                  <td>(0.256)</td>
                  <td>(0.351)</td>
                </tr>
                <tr>
                  <td>TAN attitudes (cont.)</td>
                  <td>-0.065+</td>
                  <td>0.009</td>
                  <td>-0.078+</td>
                  <td>0.118*</td>
                  <td>0.130*</td>
                  <td>0.154*</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.038)</td>
                  <td>(0.060)</td>
                  <td>(0.043)</td>
                  <td>(0.046)</td>
                  <td>(0.055)</td>
                  <td>(0.073)</td>
                </tr>
                <tr>
                  <td>Anti-redistribution attitude (cont.)</td>
                  <td>-0.038</td>
                  <td>0.002</td>
                  <td>-0.072+</td>
                  <td>-0.017</td>
                  <td>-0.001</td>
                  <td>-0.123+</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.033)</td>
                  <td>(0.052)</td>
                  <td>(0.038)</td>
                  <td>(0.040)</td>
                  <td>(0.047)</td>
                  <td>(0.064)</td>
                </tr>
                <tr>
                  <td>Age</td>
                  <td>-0.011***</td>
                  <td>-0.010+</td>
                  <td>-0.014***</td>
                  <td>-0.000</td>
                  <td>0.001</td>
                  <td>-0.008</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.003)</td>
                  <td>(0.005)</td>
                  <td>(0.004)</td>
                  <td>(0.004)</td>
                  <td>(0.005)</td>
                  <td>(0.007)</td>
                </tr>
                <tr>
                  <td>Household-equivalized income</td>
                  <td>0.000</td>
                  <td>-0.000</td>
                  <td>0.000*</td>
                  <td>0.000</td>
                  <td>0.000</td>
                  <td>0.000</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                </tr>
                <tr>
                  <td>With migration background</td>
                  <td>0.021</td>
                  <td>-0.341</td>
                  <td>0.096</td>
                  <td>0.111</td>
                  <td>0.021</td>
                  <td>0.063</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.139)</td>
                  <td>(0.247)</td>
                  <td>(0.155)</td>
                  <td>(0.173)</td>
                  <td>(0.211)</td>
                  <td>(0.275)</td>
                </tr>
                <tr>
                  <td>Suburbs/small city (ref. country)</td>
                  <td>0.017</td>
                  <td>0.297</td>
                  <td>-0.041</td>
                  <td>0.129</td>
                  <td>-0.039</td>
                  <td>0.557+</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.128)</td>
                  <td>(0.222)</td>
                  <td>(0.143)</td>
                  <td>(0.164)</td>
                  <td>(0.192)</td>
                  <td>(0.289)</td>
                </tr>
                <tr>
                  <td>Big city (ref. country)</td>
                  <td>0.130</td>
                  <td>0.523*</td>
                  <td>-0.048</td>
                  <td>0.391*</td>
                  <td>0.335</td>
                  <td>0.565+</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.142)</td>
                  <td>(0.238)</td>
                  <td>(0.162)</td>
                  <td>(0.179)</td>
                  <td>(0.207)</td>
                  <td>(0.317)</td>
                </tr>
                <tr>
                  <td>Num.Obs.</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                </tr>
                <tr>
                  <td>AIC</td>
                  <td>2710.2</td>
                  <td>1329.2</td>
                  <td>2206.9</td>
                  <td>1949.3</td>
                  <td>1495.8</td>
                  <td>918.1</td>
                </tr>
                <tr>
                  <td>BIC</td>
                  <td>2777.3</td>
                  <td>1396.4</td>
                  <td>2274.0</td>
                  <td>2016.4</td>
                  <td>1562.9</td>
                  <td>985.2</td>
                </tr>
                <tr>
                  <td>Log.Lik.</td>
                  <td>-1344.098</td>
                  <td>-653.621</td>
                  <td>-1092.461</td>
                  <td>-963.662</td>
                  <td>-736.899</td>
                  <td>-448.031</td>
                </tr>
                <tr>
                  <td>F</td>
                  <td>4.900</td>
                  <td>1.493</td>
                  <td>6.684</td>
                  <td>1.819</td>
                  <td>1.332</td>
                  <td>1.995</td>
                </tr>
                <tr>
                  <td>RMSE</td>
                  <td>0.35</td>
                  <td>0.22</td>
                  <td>0.31</td>
                  <td>0.28</td>
                  <td>0.24</td>
                  <td>0.17</td>
                </tr>
        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
<div id="table-c7" class="section level2">
<h2>Table C7</h2>
<pre class="r"><code>cm &lt;- c(
  &quot;galtan_groupsocial conservative&quot; = &quot;Social conservative group&quot;,
  &quot;econlr_groupecon. right&quot; = &quot;Econ. right group&quot;,
  &quot;galtan_att&quot; = &quot;TAN attitudes (cont.)&quot;,
  &quot;I(galtan_att^2)&quot; = &quot;TAN attitudes^2&quot;,
  &quot;poc_win:galtan_att&quot; = &quot;Migrant winners x TAN att.&quot;,
  &quot;frauen_win:galtan_att&quot; = &quot;Women winners x TAN att.&quot;,
  &quot;att_socialben&quot; = &quot;Anti-redistribution attitude (cont.)&quot;,
  &quot;age&quot; = &quot;Age&quot;,
  &quot;gender_fmwomen&quot; = &quot;Woman&quot;,
  &quot;edumedium education&quot; = &quot;Medium education (ref. low)&quot;,
  &quot;eduhigh education&quot; = &quot;High education (ref. low)&quot;,
  &quot;inc_hh&quot; = &quot;Household-equivalized income&quot;,
  &quot;migrationbyes&quot; = &quot;With migration background&quot;,
  &quot;urbansuburb/small city&quot; = &quot;Suburbs/small city (ref. country)&quot;,
  &quot;urbanbig city&quot; = &quot;Big city (ref. country)&quot;
)

model_soceconl2 &lt;- glm(socioeconomic ~ galtan_att + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_soceconl2b &lt;- glm(socioeconomic ~ galtan_att + I(galtan_att^2) + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_soceconl3 &lt;- glm(socioeconomic ~ galtan_group + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_losedict)

model_soceconw2 &lt;- glm(socioeconomic ~ galtan_att + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)

model_soceconw2b &lt;- glm(socioeconomic ~ galtan_att + I(galtan_att^2) + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)

model_soceconw3 &lt;- glm(socioeconomic ~ galtan_group + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh,
                 family = &quot;binomial&quot;,
                 data = df_p_windict)


models &lt;- list(
  &quot;Winning&quot; = model_soceconw3,
  &quot;Winning 2&quot; = model_soceconw2,
  &quot;Winning 3&quot; = model_soceconw2b,
  &quot;Losing&quot; = model_soceconl3,
  &quot;Losing 2&quot; = model_soceconl2,
  &quot;Losing 3&quot; = model_soceconl2b
  )

modelsummary(models, coef_map = cm, stars = T,
             #output = &quot;latex&quot;,
             title = &quot;Predictors of mentioning socioeconomic dictionary dimensions&quot;,
             notes = &quot;Binomial generalized linear model. Standard errors in parentheses, conf. level: 0.95.&quot;)</code></pre>
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        <thead>
        <caption>Predictors of mentioning socioeconomic dictionary dimensions</caption>
              <tr>
                <th scope="col"> </th>
                <th scope="col">Winning</th>
                <th scope="col">Winning 2</th>
                <th scope="col">Winning 3</th>
                <th scope="col">Losing</th>
                <th scope="col">Losing 2</th>
                <th scope="col">Losing 3</th>
              </tr>
        </thead>
        <tfoot><tr><td colspan="7">+ p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td></tr>
<tr><td colspan="7">Binomial generalized linear model. Standard errors in parentheses, conf. level: 0.95.</td></tr></tfoot>
        <tbody>
                <tr>
                  <td>Social conservative group</td>
                  <td>-0.204*</td>
                  <td></td>
                  <td></td>
                  <td>-0.211*</td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.083)</td>
                  <td></td>
                  <td></td>
                  <td>(0.082)</td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>TAN attitudes (cont.)</td>
                  <td></td>
                  <td>-0.086**</td>
                  <td>-0.007</td>
                  <td></td>
                  <td>-0.067*</td>
                  <td>-0.053</td>
                </tr>
                <tr>
                  <td></td>
                  <td></td>
                  <td>(0.027)</td>
                  <td>(0.109)</td>
                  <td></td>
                  <td>(0.027)</td>
                  <td>(0.107)</td>
                </tr>
                <tr>
                  <td>TAN attitudes^2</td>
                  <td></td>
                  <td></td>
                  <td>-0.010</td>
                  <td></td>
                  <td></td>
                  <td>-0.002</td>
                </tr>
                <tr>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td>(0.014)</td>
                  <td></td>
                  <td></td>
                  <td>(0.014)</td>
                </tr>
                <tr>
                  <td>Anti-redistribution attitude (cont.)</td>
                  <td>0.008</td>
                  <td>0.022</td>
                  <td>0.020</td>
                  <td>-0.010</td>
                  <td>-0.005</td>
                  <td>-0.006</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.023)</td>
                  <td>(0.024)</td>
                  <td>(0.024)</td>
                  <td>(0.022)</td>
                  <td>(0.023)</td>
                  <td>(0.024)</td>
                </tr>
                <tr>
                  <td>Age</td>
                  <td>0.001</td>
                  <td>0.001</td>
                  <td>0.001</td>
                  <td>0.004</td>
                  <td>0.004</td>
                  <td>0.004</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.002)</td>
                  <td>(0.002)</td>
                  <td>(0.002)</td>
                  <td>(0.002)</td>
                  <td>(0.002)</td>
                  <td>(0.002)</td>
                </tr>
                <tr>
                  <td>Woman</td>
                  <td>0.212**</td>
                  <td>0.208**</td>
                  <td>0.209**</td>
                  <td>0.171*</td>
                  <td>0.169*</td>
                  <td>0.169*</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.074)</td>
                  <td>(0.074)</td>
                  <td>(0.074)</td>
                  <td>(0.073)</td>
                  <td>(0.073)</td>
                  <td>(0.073)</td>
                </tr>
                <tr>
                  <td>Medium education (ref. low)</td>
                  <td>0.407***</td>
                  <td>0.400***</td>
                  <td>0.399***</td>
                  <td>0.109</td>
                  <td>0.109</td>
                  <td>0.108</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.101)</td>
                  <td>(0.101)</td>
                  <td>(0.101)</td>
                  <td>(0.097)</td>
                  <td>(0.097)</td>
                  <td>(0.097)</td>
                </tr>
                <tr>
                  <td>High education (ref. low)</td>
                  <td>0.458***</td>
                  <td>0.442***</td>
                  <td>0.443***</td>
                  <td>0.219+</td>
                  <td>0.217+</td>
                  <td>0.218+</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.118)</td>
                  <td>(0.118)</td>
                  <td>(0.118)</td>
                  <td>(0.114)</td>
                  <td>(0.114)</td>
                  <td>(0.114)</td>
                </tr>
                <tr>
                  <td>Household-equivalized income</td>
                  <td>0.000*</td>
                  <td>0.000+</td>
                  <td>0.000+</td>
                  <td>0.000*</td>
                  <td>0.000*</td>
                  <td>0.000*</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                </tr>
                <tr>
                  <td>With migration background</td>
                  <td>-0.191+</td>
                  <td>-0.188+</td>
                  <td>-0.190+</td>
                  <td>-0.316**</td>
                  <td>-0.315**</td>
                  <td>-0.315**</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.107)</td>
                  <td>(0.107)</td>
                  <td>(0.107)</td>
                  <td>(0.107)</td>
                  <td>(0.107)</td>
                  <td>(0.107)</td>
                </tr>
                <tr>
                  <td>Suburbs/small city (ref. country)</td>
                  <td>-0.028</td>
                  <td>-0.032</td>
                  <td>-0.032</td>
                  <td>0.055</td>
                  <td>0.053</td>
                  <td>0.053</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.091)</td>
                  <td>(0.091)</td>
                  <td>(0.091)</td>
                  <td>(0.089)</td>
                  <td>(0.089)</td>
                  <td>(0.090)</td>
                </tr>
                <tr>
                  <td>Big city (ref. country)</td>
                  <td>-0.159</td>
                  <td>-0.159</td>
                  <td>-0.159</td>
                  <td>-0.277**</td>
                  <td>-0.276**</td>
                  <td>-0.276**</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.104)</td>
                  <td>(0.105)</td>
                  <td>(0.105)</td>
                  <td>(0.104)</td>
                  <td>(0.104)</td>
                  <td>(0.104)</td>
                </tr>
                <tr>
                  <td>Num.Obs.</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                  <td>3301</td>
                </tr>
                <tr>
                  <td>AIC</td>
                  <td>4333.9</td>
                  <td>4330.1</td>
                  <td>4331.5</td>
                  <td>4419.1</td>
                  <td>4419.7</td>
                  <td>4421.6</td>
                </tr>
                <tr>
                  <td>BIC</td>
                  <td>4401.0</td>
                  <td>4397.2</td>
                  <td>4404.8</td>
                  <td>4486.3</td>
                  <td>4486.8</td>
                  <td>4494.9</td>
                </tr>
                <tr>
                  <td>Log.Lik.</td>
                  <td>-2155.934</td>
                  <td>-2154.041</td>
                  <td>-2153.764</td>
                  <td>-2198.570</td>
                  <td>-2198.832</td>
                  <td>-2198.824</td>
                </tr>
                <tr>
                  <td>F</td>
                  <td>5.381</td>
                  <td>5.742</td>
                  <td>5.257</td>
                  <td>5.296</td>
                  <td>5.243</td>
                  <td>4.767</td>
                </tr>
                <tr>
                  <td>RMSE</td>
                  <td>0.48</td>
                  <td>0.48</td>
                  <td>0.48</td>
                  <td>0.49</td>
                  <td>0.49</td>
                  <td>0.49</td>
                </tr>
        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
<div id="figure-c7" class="section level2">
<h2>Figure C7</h2>
<pre class="r"><code>dict_dir &lt;- dictionary(list(
  frauen = c(&quot;*frauen*&quot;, &quot;frau&quot;, &quot;feministen&quot;, &quot;feministinnen&quot;),
  männer = c(&quot;*männer&quot;, &quot;mann&quot;),
  poc = c(&quot;*migrant*&quot;, &quot;migrationshintergrund*&quot;, &quot;ausländ*&quot;, &quot;zuwander*&quot;, &quot;flüchtling*&quot;, &quot;kanacke*&quot;, &quot;einwander*&quot;, &quot;geflüchtet*&quot;, &quot;asylant*&quot;, &quot;fremd* herkunft*&quot;, &quot;fliehen&quot;, &quot;geflohen*&quot;, &quot;bipoc&quot;, &quot;bpoc&quot;, &quot;poc&quot;),
  weiss = c(&quot;deutsch*&quot;, &quot;weiße&quot;, &quot;weisse&quot;, &quot;deutschland&quot;, &quot;volk*&quot;, &quot;einheimische*&quot;, &quot;bevölkerung*&quot;, &quot;land&quot;
            )
))

df_win_dict_dir &lt;- tokens_lookup(toks_win_comp, dict_dir, valuetype = &quot;glob&quot;) %&gt;% 
  dfm() %&gt;% dfm_weight(&quot;boolean&quot;) %&gt;% 
  convert(to = &quot;data.frame&quot;) %&gt;% 
  bind_cols(docvars(toks_win_comp, &quot;ResponseId&quot;)) %&gt;% 
  rename(ResponseId = last_col()) %&gt;% 
  select(-doc_id) %&gt;% 
  rename_at(vars(-ResponseId), ~paste0(., &#39;_win&#39;))

df_lose_dict_dir &lt;- tokens_lookup(toks_lose_comp, dict_dir, 
                                  valuetype = &quot;glob&quot;) %&gt;% 
  dfm() %&gt;% dfm_weight(&quot;boolean&quot;) %&gt;% 
  convert(to = &quot;data.frame&quot;) %&gt;% 
  bind_cols(docvars(toks_lose_comp, &quot;ResponseId&quot;)) %&gt;% 
  rename(ResponseId = last_col()) %&gt;% 
  select(-doc_id)%&gt;% 
  rename_at(vars(-ResponseId), ~paste0(., &#39;_lose&#39;))

df_p_win_dict_dir &lt;- left_join(df_win_dict_dir, df_p, by = &quot;ResponseId&quot;)
df_p_dict_dir &lt;- left_join(df_p_win_dict_dir, df_lose_dict_dir, by = &quot;ResponseId&quot;)

model_white3 &lt;- glm(weiss_lose ~ poc_win + galtan_att + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh, 
                 family = &quot;binomial&quot;,
                 data = df_p_dict_dir)

model_white4 &lt;- glm(weiss_lose ~ poc_win * galtan_att + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh, 
                 family = &quot;binomial&quot;,
                 data = df_p_dict_dir)

pw &lt;- plot_predictions(model_white4, condition = c(&quot;galtan_att&quot;,&quot;poc_win&quot;)) +
  labs(subtitle = &quot;Race zero-sum test&quot;, x = &quot;GAL-TAN attitudes&quot;,
       y = &quot;Pred. probability to mention natives as losers&quot;, 
       color = &quot;Mentioning migrants/PoC as winners&quot;, fill = &quot;Mentioning migrants/PoC as winners&quot;) +
  theme(legend.position = &quot;bottom&quot;)

model_men3 &lt;- glm(männer_lose ~ frauen_win + galtan_att + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh, 
                 family = &quot;binomial&quot;,
                 data = df_p_dict_dir)

model_men4 &lt;- glm(männer_lose ~ frauen_win * galtan_att + att_socialben + age + gender_fm + edu + migrationb + urban + inc_hh, 
                 family = &quot;binomial&quot;,
                 data = df_p_dict_dir)

pm &lt;- plot_predictions(model_men4, condition = c(&quot;galtan_att&quot;, &quot;frauen_win&quot;)) +
  labs(subtitle = &quot;Gender zero-sum test&quot;, x = &quot;GAL-TAN attitudes&quot;,
       y = &quot;Pred. probability to mention men as losers&quot;, 
       color = &quot;Mentioning women as winners&quot;, fill = &quot;Mentioning women as winners&quot;) +
  theme(legend.position = &quot;bottom&quot;)</code></pre>
<pre class="r"><code>plotszerosum &lt;- arrangeGrob(pw, pm, nrow = 2,
                            heights = unit(c(10, 10), c(&quot;cm&quot;)))
grid.arrange(plotszerosum)</code></pre>
<p><img role="img" src="data:image/png;base64,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width="480" /></p>
</div>
<div id="table-c9" class="section level2">
<h2>Table C9</h2>
<pre class="r"><code>cm &lt;- c(
  &quot;poc_win&quot; = &quot;Mentioned migrants as winners&quot;,
  &quot;poc_win:galtan_att&quot; = &quot;Migrant winners x TAN att.&quot;,
  &quot;männer_lose&quot; = &quot;Mentioned men as losers&quot;,
  &quot;frauen_win&quot; = &quot;Mentioned women as winners&quot;,
  &quot;frauen_win:galtan_att&quot; = &quot;Women winners x TAN att.&quot;,
  &quot;galtan_groupsocial conservative&quot; = &quot;Social conservative group&quot;,
  &quot;econlr_groupecon. right&quot; = &quot;Econ. right group&quot;,
  &quot;galtan_att&quot; = &quot;TAN attitudes (cont.)&quot;,
  &quot;I(galtan_att^2)&quot; = &quot;TAN attitudes^2&quot;,
  &quot;att_socialben&quot; = &quot;Anti-redistribution attitude (cont.)&quot;,
  &quot;age&quot; = &quot;Age&quot;,
  &quot;gender_fmwomen&quot; = &quot;Woman&quot;,
  &quot;edumedium education&quot; = &quot;Medium education (ref. low)&quot;,
  &quot;eduhigh education&quot; = &quot;High education (ref. low)&quot;,
  &quot;inc_hh&quot; = &quot;Household-equivalized income&quot;,
  &quot;migrationbyes&quot; = &quot;With migration background&quot;,
  &quot;urbansuburb/small city&quot; = &quot;Suburbs/small city (ref. country)&quot;,
  &quot;urbanbig city&quot; = &quot;Big city (ref. country)&quot;
)

models &lt;- list(
  &quot;Natives losing 1&quot; = model_white3,
  &quot;Natives losing 2&quot; = model_white4,
  &quot;Men losing 1&quot; = model_men3,
  &quot;Men losing 2&quot; = model_men4
)

modelsummary(models, coef_map = cm, stars = T,
             #output = &quot;latex&quot;,
             title = &quot;Predictors of mentioning natives/men as losers, by mentioning migrants/women as winners&quot;,
             notes = &quot;Binomial generalized linear model. Standard errors in parentheses, conf. level: 0.95.&quot;)</code></pre>
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        <thead>
        <caption>Predictors of mentioning natives/men as losers, by mentioning migrants/women as winners</caption>
              <tr>
                <th scope="col"> </th>
                <th scope="col">Natives losing 1</th>
                <th scope="col">Natives losing 2</th>
                <th scope="col">Men losing 1</th>
                <th scope="col">Men losing 2</th>
              </tr>
        </thead>
        <tfoot><tr><td colspan="5">+ p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td></tr>
<tr><td colspan="5">Binomial generalized linear model. Standard errors in parentheses, conf. level: 0.95.</td></tr></tfoot>
        <tbody>
                <tr>
                  <td>Mentioned migrants as winners</td>
                  <td>2.286***</td>
                  <td>1.603*</td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.234)</td>
                  <td>(0.665)</td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>Migrant winners x TAN att.</td>
                  <td></td>
                  <td>0.164</td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td></td>
                  <td></td>
                  <td>(0.148)</td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>Mentioned women as winners</td>
                  <td></td>
                  <td></td>
                  <td>2.478***</td>
                  <td>3.093***</td>
                </tr>
                <tr>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td>(0.284)</td>
                  <td>(0.666)</td>
                </tr>
                <tr>
                  <td>Women winners x TAN att.</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td>-0.177</td>
                </tr>
                <tr>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td>(0.174)</td>
                </tr>
                <tr>
                  <td>TAN attitudes (cont.)</td>
                  <td>0.177*</td>
                  <td>0.143+</td>
                  <td>0.139</td>
                  <td>0.206+</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.071)</td>
                  <td>(0.077)</td>
                  <td>(0.099)</td>
                  <td>(0.119)</td>
                </tr>
                <tr>
                  <td>Anti-redistribution attitude (cont.)</td>
                  <td>0.010</td>
                  <td>0.007</td>
                  <td>-0.133</td>
                  <td>-0.130</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.060)</td>
                  <td>(0.061)</td>
                  <td>(0.088)</td>
                  <td>(0.088)</td>
                </tr>
                <tr>
                  <td>Age</td>
                  <td>0.014*</td>
                  <td>0.014*</td>
                  <td>-0.008</td>
                  <td>-0.008</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.007)</td>
                  <td>(0.007)</td>
                  <td>(0.009)</td>
                  <td>(0.009)</td>
                </tr>
                <tr>
                  <td>Woman</td>
                  <td>0.324+</td>
                  <td>0.316</td>
                  <td>-0.432</td>
                  <td>-0.445</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.196)</td>
                  <td>(0.196)</td>
                  <td>(0.278)</td>
                  <td>(0.279)</td>
                </tr>
                <tr>
                  <td>Medium education (ref. low)</td>
                  <td>0.428</td>
                  <td>0.436</td>
                  <td>0.497</td>
                  <td>0.475</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.284)</td>
                  <td>(0.286)</td>
                  <td>(0.406)</td>
                  <td>(0.406)</td>
                </tr>
                <tr>
                  <td>High education (ref. low)</td>
                  <td>0.595+</td>
                  <td>0.605+</td>
                  <td>0.383</td>
                  <td>0.352</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.323)</td>
                  <td>(0.324)</td>
                  <td>(0.466)</td>
                  <td>(0.465)</td>
                </tr>
                <tr>
                  <td>Household-equivalized income</td>
                  <td>0.000</td>
                  <td>0.000</td>
                  <td>0.000</td>
                  <td>0.000</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                  <td>(0.000)</td>
                </tr>
                <tr>
                  <td>With migration background</td>
                  <td>-0.379</td>
                  <td>-0.374</td>
                  <td>-0.497</td>
                  <td>-0.477</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.318)</td>
                  <td>(0.318)</td>
                  <td>(0.448)</td>
                  <td>(0.447)</td>
                </tr>
                <tr>
                  <td>Suburbs/small city (ref. country)</td>
                  <td>-0.060</td>
                  <td>-0.072</td>
                  <td>0.022</td>
                  <td>0.034</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.250)</td>
                  <td>(0.251)</td>
                  <td>(0.347)</td>
                  <td>(0.348)</td>
                </tr>
                <tr>
                  <td>Big city (ref. country)</td>
                  <td>0.453+</td>
                  <td>0.443+</td>
                  <td>0.198</td>
                  <td>0.205</td>
                </tr>
                <tr>
                  <td></td>
                  <td>(0.264)</td>
                  <td>(0.265)</td>
                  <td>(0.382)</td>
                  <td>(0.382)</td>
                </tr>
                <tr>
                  <td>Num.Obs.</td>
                  <td>3287</td>
                  <td>3287</td>
                  <td>3287</td>
                  <td>3287</td>
                </tr>
                <tr>
                  <td>AIC</td>
                  <td>972.6</td>
                  <td>973.3</td>
                  <td>551.4</td>
                  <td>552.4</td>
                </tr>
                <tr>
                  <td>BIC</td>
                  <td>1045.8</td>
                  <td>1052.6</td>
                  <td>624.6</td>
                  <td>631.6</td>
                </tr>
                <tr>
                  <td>Log.Lik.</td>
                  <td>-474.305</td>
                  <td>-473.669</td>
                  <td>-263.707</td>
                  <td>-263.180</td>
                </tr>
                <tr>
                  <td>RMSE</td>
                  <td>0.18</td>
                  <td>0.18</td>
                  <td>0.13</td>
                  <td>0.13</td>
                </tr>
        </tbody>
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